A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning

Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth inc...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of medical systems Jg. 43; H. 11; S. 326
Hauptverfasser:	Amin, Javeria, Sharif, Muhammad, Yasmin, Mussarat, Saba, Tanzila, Anjum, Muhammad Almas, Fernandes, Steven Lawrence
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2019 Springer Nature B.V
Schlagworte:	Algorithms Brain cancer Brain damage Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Brain tumors Health Informatics Health Informatics and Computer Vision Health Sciences Humans Image & Signal Processing Image classification Image enhancement Image processing Image Processing, Computer-Assisted - methods Image segmentation Ischemia Magnetic Resonance Imaging - methods Medical imaging Medicine Medicine & Public Health Neural Networks, Computer Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Tomography, X-Ray Computed - methods Transfer learning Tumor cells Tumors Segmentation Alex network Fused score vector Classification Google network Brain tumor detection Softmax layer
ISSN:	0148-5598, 1573-689X, 1573-689X
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively.
AbstractList	Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively. Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively.Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain. It affects the brain by squeezing/ damaging healthy tissues. It also amplifies intra cranial pressure and as a result tumor cells growth increases rapidly which may lead to death. It is, therefore desirable to diagnose/ detect brain tumor at an early stage that may increase the patient survival rate. The major objective of this research work is to present a new technique for the detection of tumor. The proposed architecture accurately segments and classifies the benign and malignant tumor cases. Different spatial domain methods are applied to enhance and accurately segment the input images. Moreover Alex and Google networks are utilized for classification in which two score vectors are obtained after the softmax layer. Further, both score vectors are fused and supplied to multiple classifiers along with softmax layer. Evaluation of proposed model is done on top medical image computing and computer-assisted intervention (MICCAI) challenge datasets i.e., multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016 and ischemic stroke lesion segmentation (ISLES) 2018 respectively.
ArticleNumber	326
Author	Fernandes, Steven Lawrence Saba, Tanzila Amin, Javeria Yasmin, Mussarat Anjum, Muhammad Almas Sharif, Muhammad
Author_xml	– sequence: 1 givenname: Javeria surname: Amin fullname: Amin, Javeria email: javeria.amin@uow.edu.pk organization: Department of Computer Science, University of Wah, Department of Computer Science, COMSATS University Islamabad, Wah Campus Pakistan – sequence: 2 givenname: Muhammad surname: Sharif fullname: Sharif, Muhammad organization: Department of Computer Science, COMSATS University Islamabad, Wah Campus Pakistan – sequence: 3 givenname: Mussarat surname: Yasmin fullname: Yasmin, Mussarat organization: Department of Computer Science, COMSATS University Islamabad, Wah Campus Pakistan – sequence: 4 givenname: Tanzila surname: Saba fullname: Saba, Tanzila organization: College of Computer and Information Sciences, Prince Sultan University – sequence: 5 givenname: Muhammad Almas surname: Anjum fullname: Anjum, Muhammad Almas organization: College of EME, NUST Pakistan – sequence: 6 givenname: Steven Lawrence surname: Fernandes fullname: Fernandes, Steven Lawrence organization: Department of Electronics and Communication Engineering, Sahyadri College of Engineering & Management
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31643004$$D View this record in MEDLINE/PubMed
BookMark	eNp9kU9r3DAQxUVJaTZJP0AvRdBLL25H1h9Lx83StIUlOWQDvQlZHm8dbHkr2Sn99tXihEKgOWmY-b1h9N4ZOQljQELeMfjEAKrPiYFhqgBmCiYkL_QrsmKy4oXS5scJWQETupDS6FNyltI9ABilqjfklDMlOIBYkbim1_ibrg-HODr_k7ZjpJfRdYHu5iHXt7gfMExu6sZAXWjopncpdW3nl9alS9jQXNz6MSLd4gP29GpOx9ld6sKe7qILqcWYZy6G3Lkgr1vXJ3z7-J6Tu6svu823Ynvz9ftmvS08r8qpqI3WuqlRGMEEyKZthFa-lp55UyotXeVq7bSua2wVOKUdcuVQNSARBDP8nHxc9uav_ZoxTXbokse-dwHHOdmSg2YVkyXP6Idn6P04x5CvO1KV4ZIZyNT7R2quB2zsIXaDi3_sk5sZqBbAxzGliK313WLdlC3tLQN7zM0uudmcmz3mZnVWsmfKp-UvacpFkzIb9hj_Hf1_0V9qjakf
CitedBy_id	crossref_primary_10_1002_cpe_7543 crossref_primary_10_1016_j_cmpb_2021_106539 crossref_primary_10_1186_s40537_024_01050_0 crossref_primary_10_3390_jpm12020275 crossref_primary_10_1002_cpe_7100 crossref_primary_10_1016_j_compbiomed_2024_108503 crossref_primary_10_1007_s12553_020_00514_6 crossref_primary_10_1007_s00521_025_11013_y crossref_primary_10_1007_s00521_023_08717_4 crossref_primary_10_1080_13682199_2023_2211890 crossref_primary_10_13005_bpj_2124 crossref_primary_10_1016_j_compeleceng_2022_108386 crossref_primary_10_1016_j_compmedimag_2021_101940 crossref_primary_10_1016_j_brainres_2023_148300 crossref_primary_10_1080_21681163_2022_2124546 crossref_primary_10_1002_jemt_23429 crossref_primary_10_1080_0954898X_2023_2225601 crossref_primary_10_32604_cmc_2022_019115 crossref_primary_10_1016_j_bspc_2024_107180 crossref_primary_10_1007_s00521_022_07388_x crossref_primary_10_3390_jimaging11010002 crossref_primary_10_1007_s40747_021_00563_y crossref_primary_10_4103_jmp_jmp_52_22 crossref_primary_10_1007_s11042_022_13107_4 crossref_primary_10_3390_bioengineering11121265 crossref_primary_10_1016_j_rineng_2024_102459 crossref_primary_10_3390_info13060268 crossref_primary_10_1007_s11042_024_20353_1 crossref_primary_10_1002_jemt_24054 crossref_primary_10_1016_j_patrec_2019_12_006 crossref_primary_10_3389_frai_2022_780405 crossref_primary_10_1007_s10586_022_03707_y crossref_primary_10_1111_exsy_13225 crossref_primary_10_3390_cancers13215546 crossref_primary_10_1007_s11042_025_20702_8 crossref_primary_10_1109_ACCESS_2020_3045732 crossref_primary_10_3390_diagnostics12040823 crossref_primary_10_1007_s13721_019_0209_1 crossref_primary_10_3390_app13063680 crossref_primary_10_1016_j_compbiomed_2022_105539 crossref_primary_10_3390_jimaging7040066 crossref_primary_10_1109_ACCESS_2020_3021660 crossref_primary_10_3390_bioengineering11050497 crossref_primary_10_1007_s11042_023_17738_z crossref_primary_10_1007_s12559_022_10096_2 crossref_primary_10_1016_j_rineng_2025_105154 crossref_primary_10_1088_2057_1976_ac0ccc crossref_primary_10_1016_j_heliyon_2024_e37804 crossref_primary_10_1007_s12559_021_09926_6 crossref_primary_10_1186_s41824_024_00195_8 crossref_primary_10_1002_acm2_70012 crossref_primary_10_3390_jpm12091454 crossref_primary_10_3390_electronics12030590 crossref_primary_10_1007_s44196_025_00772_0 crossref_primary_10_1016_j_ejrad_2022_110639 crossref_primary_10_1007_s42600_022_00214_2 crossref_primary_10_1111_exsy_13510 crossref_primary_10_1007_s11760_020_01793_2 crossref_primary_10_1002_ima_22831 crossref_primary_10_1007_s11831_022_09758_z crossref_primary_10_1080_01969722_2020_1871231 crossref_primary_10_32604_cmc_2022_018562 crossref_primary_10_1109_JBHI_2023_3270861 crossref_primary_10_1007_s11548_021_02326_z crossref_primary_10_1007_s40747_021_00310_3 crossref_primary_10_32604_cmc_2023_035860 crossref_primary_10_1007_s40747_021_00564_x crossref_primary_10_1080_13682199_2023_2283678 crossref_primary_10_4018_IJEHMC_315730 crossref_primary_10_3390_app11041675 crossref_primary_10_1002_cpe_6821 crossref_primary_10_1038_s41598_022_07733_z crossref_primary_10_1016_j_inffus_2024_102424 crossref_primary_10_1016_j_neucom_2021_08_159 crossref_primary_10_1016_j_jocn_2021_04_043 crossref_primary_10_1109_ACCESS_2023_3347545 crossref_primary_10_3233_IDA_240069 crossref_primary_10_3390_life12081126 crossref_primary_10_32604_cmc_2023_032816 crossref_primary_10_1016_j_bspc_2022_104027 crossref_primary_10_3390_app13053108 crossref_primary_10_1111_exsy_13200 crossref_primary_10_1016_j_bbe_2021_11_004 crossref_primary_10_3389_fpubh_2022_819865 crossref_primary_10_3390_diagnostics13111832 crossref_primary_10_1007_s11042_020_10423_5 crossref_primary_10_1016_j_bspc_2022_104017 crossref_primary_10_1007_s40747_021_00321_0 crossref_primary_10_1111_exsy_13281 crossref_primary_10_1111_coin_70018
Cites_doi	10.1109/TIP.2017.2760512 10.1109/TIP.2013.2278465 10.1016/j.media.2016.07.009 10.1007/s12652-018-0854-8 10.1016/j.future.2018.05.002 10.1016/j.future.2018.04.065 10.1167/17.10.296 10.1016/j.media.2016.05.004 10.1007/978-981-13-1819-1_51 10.1117/12.2083596 10.1007/978-3-319-60964-5_44 10.1016/j.patrec.2017.06.021 10.1016/j.media.2016.10.004 10.2196/jmir.2930 10.1016/j.patrec.2017.10.036 10.1016/j.sigpro.2012.05.012 10.1007/978-3-319-55524-9_14 10.1155/2019/8415485 10.1016/j.jocs.2017.01.002 10.1088/0031-9155/58/13/R97 10.1016/j.patrec.2018.01.021 10.1016/j.eij.2015.01.003 10.1016/j.future.2018.04.074 10.1155/2016/8356294 10.1016/j.artmed.2018.08.008 10.1109/BMEI.2010.5639536 10.1007/978-3-319-55524-9_13 10.1016/j.mri.2013.05.002 10.1109/TIP.2017.2781424 10.1016/j.patrec.2017.05.028 10.1259/bjr/65711810 10.1109/34.295913 10.1166/jmihi.2018.2502 10.2174/1574893614666181220094918 10.1016/j.media.2017.10.002 10.1007/978-981-10-4280-5_33 10.1007/978-3-540-85988-8_43 10.1142/S0219519418500380 10.1109/TMI.2014.2377694 10.1016/j.patcog.2018.11.009 10.1007/978-3-642-15745-5_19 10.2174/1573405613666170306114320
ContentType	Journal Article
Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2019 Journal of Medical Systems is a copyright of Springer, (2019). All Rights Reserved.
Copyright_xml	– notice: Springer Science+Business Media, LLC, part of Springer Nature 2019 – notice: Journal of Medical Systems is a copyright of Springer, (2019). All Rights Reserved.
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QF 7QO 7QQ 7RV 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 7X7 7XB 88C 88E 88I 8AL 8AO 8BQ 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU DWQXO F28 FR3 FYUFA GHDGH GNUQQ H8D H8G HCIFZ JG9 JQ2 K7- K9. KB0 KR7 L7M LK8 L~C L~D M0N M0S M0T M1P M2P M7P NAPCQ P5Z P62 P64 PHGZM PHGZT PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8
DOI	10.1007/s10916-019-1453-8
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts ProQuest Nursing & Allied Health Database Computer and Information Systems Abstracts Corrosion Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts Materials Business File Mechanical & Transportation Engineering Abstracts Neurosciences Abstracts Solid State and Superconductivity Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Healthcare Administration Database (Alumni) Medical Database (Alumni Edition) Science Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection METADEX Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Database ProQuest Central Technology Collection Natural Science Collection ProQuest One ProQuest Central ANTE: Abstracts in New Technology & Engineering Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student Aerospace Database Copper Technical Reference Library SciTech Premium Collection Materials Research Database ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) Civil Engineering Abstracts Advanced Technologies Database with Aerospace Biological Sciences Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database Health & Medical Collection (Alumni) ProQuest Healthcare Administration Database Medical Database Science Database Biological Science Database Nursing & Allied Health Premium Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Materials Research Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China Materials Business File ProQuest One Applied & Life Sciences Engineered Materials Abstracts Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ANTE: Abstracts in New Technology & Engineering Advanced Technologies & Aerospace Collection Aluminium Industry Abstracts ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Electronics & Communications Abstracts ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Ceramic Abstracts Biological Science Database Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Health Management (Alumni Edition) ProQuest Nursing & Allied Health Source (Alumni) Solid State and Superconductivity Abstracts Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) Mechanical & Transportation Engineering Abstracts ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central Aerospace Database Copper Technical Reference Library ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace Civil Engineering Abstracts ProQuest Computing ProQuest Central Basic ProQuest Science Journals ProQuest Computing (Alumni Edition) ProQuest Health Management ProQuest Nursing & Allied Health Source ProQuest SciTech Collection METADEX Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library Corrosion Abstracts ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE MEDLINE - Academic Materials Research Database
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine Public Health
EISSN	1573-689X
ExternalDocumentID	31643004 10_1007_s10916_019_1453_8
Genre	Journal Article
GroupedDBID	--- -53 -5D -5G -BR -EM -Y2 -~C .86 .GJ .VR 04C 06C 06D 0R~ 0VY 199 1SB 2.D 203 28- 29L 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 36B 3SX 3V. 4.4 406 408 409 40E 53G 5GY 5QI 5RE 5VS 67Z 6NX 77K 78A 7RV 7X7 88E 88I 8AO 8FE 8FG 8FH 8FI 8FJ 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANXM AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAWTL AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIPD ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACDTI ACGFO ACGFS ACGOD ACHSB ACHXU ACIHN ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACPRK ACREN ACUDM ACZOJ ADBBV ADHHG ADHIR ADIMF ADINQ ADJJI ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFKRA AFLOW AFQWF AFRAH AFWTZ AFYQB AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHIZS AHKAY AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALIPV ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG AQUVI ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BAPOH BBNVY BBWZM BDATZ BENPR BGLVJ BGNMA BHPHI BKEYQ BMSDO BPHCQ BSONS BVXVI CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 DWQXO EBD EBLON EBS EIHBH EIOEI EJD EMB EMOBN EN4 EPAXT ESBYG EX3 F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC FYUFA G-Y G-Z GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GRRUI GXS H13 HCIFZ HF~ HG5 HG6 HMCUK HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ IMOTQ ITM IWAJR IXC IXE IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K6V K7- KDC KOV KOW KPH LAK LK8 LLZTM M0N M0T M1P M2P M4Y M7P MA- MK0 N2Q NAPCQ NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9S PF0 PQQKQ PROAC PSQYO PT4 PT5 Q2X QOK QOR QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZD RZK S16 S1Z S26 S27 S28 S37 S3B SAP SCLPG SDE SDH SDM SHX SISQX SJYHP SMT SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SV3 SZ9 SZN T13 T16 TEORI TN5 TSG TSK TSV TT1 TUC U2A U9L UG4 UKHRP UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WH7 WJK WK8 WOW YLTOR Z45 Z7R Z7U Z7X Z7Z Z81 Z82 Z83 Z87 Z88 Z8M Z8R Z8T Z8W Z92 ZMTXR ~A9 ~EX 77I AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PJZUB PPXIY PQGLB CGR CUY CVF ECM EIF NPM 7QF 7QO 7QQ 7SC 7SE 7SP 7SR 7TA 7TB 7TK 7U5 7XB 8AL 8BQ 8FD 8FK F28 FR3 H8D H8G JG9 JQ2 K9. KR7 L7M L~C L~D P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 PUEGO
ID	FETCH-LOGICAL-c372t-b9888dbe4941405dfd486cb5c1c92685a7ab8a88bbef60a68ae36ae6d05e04193
IEDL.DBID	RSV
ISICitedReferencesCount	101
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000492183400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0148-5598 1573-689X
IngestDate	Thu Sep 04 16:37:57 EDT 2025 Wed Nov 05 01:14:21 EST 2025 Wed Feb 19 02:31:25 EST 2025 Tue Nov 18 22:42:16 EST 2025 Sat Nov 29 05:35:01 EST 2025 Fri Feb 21 02:37:17 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	11
Keywords	Segmentation Alex network Fused score vector Classification Google network Brain tumor detection Softmax layer
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c372t-b9888dbe4941405dfd486cb5c1c92685a7ab8a88bbef60a68ae36ae6d05e04193
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
PMID	31643004
PQID	2307935190
PQPubID	54050
ParticipantIDs	proquest_miscellaneous_2308171523 proquest_journals_2307935190 pubmed_primary_31643004 crossref_citationtrail_10_1007_s10916_019_1453_8 crossref_primary_10_1007_s10916_019_1453_8 springer_journals_10_1007_s10916_019_1453_8
PublicationCentury	2000
PublicationDate	2019-11-01
PublicationDateYYYYMMDD	2019-11-01
PublicationDate_xml	– month: 11 year: 2019 text: 2019-11-01 day: 01
PublicationDecade	2010
PublicationPlace	New York
PublicationPlace_xml	– name: New York – name: United States
PublicationTitle	Journal of medical systems
PublicationTitleAbbrev	J Med Syst
PublicationTitleAlternate	J Med Syst
PublicationYear	2019
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	Amorim, P.H.A.C.V.S., Escudero, G.G., Oliveira, D.D.C., Pereira, S.M., Santos, H.M., and Scussel, A.A., 3D U-nets for brain tumor segmentation in MICCAI 2017 BraTS challenge proceedings of the 6th MICCAI BraTS Challenge, 2017. RajinikanthVSatapathySCFernandesSLNachiappanSEntropy based segmentation of tumor from brain MR images–a study with teaching learning based optimizationPattern Recogn. Lett.201794879510.1016/j.patrec.2017.05.028 Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A. V., Criminisi, A., Rueckert, D., and Glocker, B., DeepMedic for brain tumor segmentation. In: International workshop on Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer, 2016, 138–149. Menze, B.H., Van Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., and Golland, P., A generative model for brain tumor segmentation in multi-modal images. International conference on medical image computing and computer-assisted intervention, Springer. 151–159, 2010. Ellwaa, A., Hussein, A., AlNaggar, E., Zidan, M., Zaki, M., Ismail, M.A., and Ghanem, N.M., Brain tumor segmantation using random forest trained on iteratively selected patients. International workshop on Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer, 129–137, 2016. AminJSharifMYasminMFernandesSLBig data analysis for brain tumor detection: Deep convolutional neural networksFut. Gen. Comput. Syst.20188729029710.1016/j.future.2018.04.065 Zhang, L., Song, M., Liu, X., Bu, J., and Chen, C.J.S.P., Fast multi-view segment graph kernel for object classification. 93 (6):1597–1607, 2013. Bernal, J., Kushibar, K., Asfaw, D. S., Valverde, S., Oliver, A., Martí, R., and Lladó, X., Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: A review. Artificial intelligence in medicine, 2018. Deng, W., Xiao, W., Deng, H., and Liu, J., MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve. Biomedical engineering and informatics (BMEI), 2010 3rd international conference on, IEEE. 393–396, 2010. AminJSharifMYasminMAliHFernandesSLA method for the detection and classification of diabetic retinopathy using structural predictors of bright lesionsJ. Comput. Sci.20171915316410.1016/j.jocs.2017.01.002 LiaqatAKhanMAShahJHSharifMYasminMFernandesSLAutomated ulcer and bleeding classification from WCE images using multiple features fusion and selectionJ. Mech. Med. Biol.20181804185003810.1142/S0219519418500380 MenzeBHJakabABauerSKalpathy-CramerJFarahaniKKirbyJBurrenYPorzNSlotboomJWiestRThe multimodal brain tumor image segmentation benchmark (BRATS)IEEE Trans. Med. Imag.20153410199310.1109/TMI.2014.2377694 HavaeiMDavyAWarde-FarleyDBiardACourvilleABengioYPalCJodoinP-MLarochelleHBrain tumor segmentation with deep neural networksMed. Image Anal.201735183110.1016/j.media.2016.05.004 Reza, S.M., and Mays, R., Iftekharuddin KM multi-fractal detrended texture feature for brain tumor classification. Proceedings of SPIE--the International Society for Optical Engineering. NIH Public Access, 2015. Shah, J.H., Sharif, M., Yasmin, M., and Fernandes, S.L., Facial expressions classification and false label reduction using LDA and threefold SVM. Pattern Recognition Letters, 2017. AnsariGJShahJHYasminMSharifMFernandesSLA novel machine learning approach for scene text extractionFut. Gen. Comput. Syst.20188732834010.1016/j.future.2018.04.074 RazaMSharifMYasminMKhanMASabaTFernandesSLAppearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learningFut. Gen. Comput. Syst.201888283910.1016/j.future.2018.05.002 Ghosh, A., Maso, F.D., Roig, M., Mitsis, G.D., and Boudrias, M.-H., Deep semantic architecture with discriminative feature visualization for neuroimage analysis. arXiv preprint arXiv:180511704, 2018. Yamashita, R., Nishio, M., Do, R.K.G., and Togashi, K., Convolutional neural networks: An overview and application in radiology. Insights into imaging:1–19, 2018. Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., and Büchler, P., The virtual skeleton database: An open access repository for biomedical research and collaboration. Journal of medical Internet research 15 (11), 2013. Alex, Krizhevsky., Sutskever, Ilya., and Hinton, GE., ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems, 2012. ChenSDingCLiuMDual-force convolutional neural networks for accurate brain tumor segmentationPattern Recogn.2019889010010.1016/j.patcog.2018.11.009 Gordillo, N., Montseny, E., and Sobrevilla, P.J., State of the art survey on MRI brain tumor segmentation. 31 (8):1426–1438, 2013. Upadhyay, N., and AJTBjor, W., Conventional MRI evaluation of gliomas. 84 (special_issue_2):S107-S111, 2011. Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y., Automatic brain tumor detection and segmentation using U-net based fully convolutional networks. Annual conference on medical image understanding and analysis. Springer, 506–517, 2017. Zhou, B., Khosla, A., Lapedriza, A., Torralba, A., and Oliva, A., Places: An image database for deep scene understanding, (2016). KamnitsasKLedigCNewcombeVFSimpsonJPKaneADMenonDKRueckertDGlockerBEfficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentationMed. Image Analy.201736617810.1016/j.media.2016.10.004 Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P.F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., and Wirkert, S., Nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:180910486, 2018. Simon Andermatt, S.P., and Cattin, P., Multi-dimensional gated recurrent units for brain tumor segmentation. Proceedings of the 6th MICCAI BraTS Challenge (2017), 1984. Van Der Kouwe, A., Brain tumor segmentation from multi modal MR images using fully convolutional neural network. Proceedings of the 6th MICCAI BraTS challenge, 2017. Abdel-MaksoudEElmogyMAl-AwadiRBrain tumor segmentation based on a hybrid clustering techniqueEgypt Inform. J.2015161718110.1016/j.eij.2015.01.003 NidaNSharifMKhanMUGYasminMFernandesSLA framework for automatic colorization of medical imagingIIOAB J.20167202209 Cui, Z., Yang, J., and Qiao, Y., Brain MRI segmentation with patch-based CNN approach. Control conference (CCC), 2016 35th Chinese. IEEE. 7026–7031, 2016. Hai, J., Qiao, K., Chen, J., Tan, H., Xu, J., Zeng, L., Shi, D., and Yan, B., Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation. Journal of Healthcare Engineering, 2019. Zhang, L., Han, Y., Yang, Y., Song, M., Yan, S., and Tian, QJIToIP., Discovering discriminative graphlets for aerial image categories recognition. 22 (12):5071–5084, 2013. ZhangCFangMNieHBrain tumor segmentation using fully convolutional networks from magnetic resonance imagingJ. Med. Imag. Health Inform.2018881546155310.1166/jmihi.2018.2502 Amin, J., Sharif, M., Yasmin, M., and Fernandes, S.L., A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognition Letters, 2017. Han, J., Quan, R., Zhang, D., and Nie, F.J.I., ToIP Robust object co-segmentation using background prior. 27 (4):1639–1651, 2018. SatapathySCFernandesSLLinHStroke lesion segmentation and analysis using entropy/Otsu’s function–a study with social group optimizationCurr. Bioinform.20191443053131:CAS:528:DC%2BC1MXhtVGhsb3J10.2174/1574893614666181220094918 Bauer, S., Wiest, R., Nolte, L.-P., Reyes, M.J., PiM, Biology. A survey of MRI-based medical image analysis for brain tumor studies. 58 (13):R97, 2013. NaqiSSharifMYasminMFernandesSLLung nodule detection using polygon approximation and hybrid features from CT imagesCurr. Med. Imag. Rev.201814110811710.2174/1573405613666170306114320 Bhagat, P., and Choudhary, P., Multiclass segmentation of brain tumor from MRI images. In: Applications of artificial intelligence techniques in engineering. Springer, 543–553, 2019. Zhao, L., and Jia K., Multiscale cnns for brain tumor segmentation and diagnosis. Computational and mathematical methods in medicine 2016. Sharif, M., Khan, M.A., Faisal, M., Yasmin, M., and Fernandes, S.L., A framework for offline signature verification system: Best features selection approach. Pattern Recognition Letters, 2018. Cheng, G., Zhou, P., and Han, JJIToIP., Duplex metric learning for image set classification. 27 (1):281–292, 2018. Raja, N.S.M., Fernandes, S., Dey, N., Satapathy, S.C., and Rajinikanth, V., Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. Journal of Ambient Intelligence and Humanized Computing:1–12, 2018. Lee, C.-H., Wang, S., Murtha, A., Brown, M.R., and Greiner, R., Segmenting brain tumors using pseudo–conditional random fields. International conference on medical image computing and computer-assisted intervention. Springer. 359–366, 2008. MaierOMenzeBHvon der GablentzJHäniLHeinrichMPLiebrandMWinzeckSBasitABentleyPChenLISLES 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRIMed. Image Analy.20173525026910.1016/j.media.2016.07.009 ZhaoXWuYSongGLiZZhangYFanYA deep learning model integrating FCNNs and CRFs for brain tumor segmentationMed. Image Analy.2018439811110.1016/j.media.2017.10.002 Adams, R., and Bischof, L.J., ITopa, intelligence m. Seeded region growing. 16 (6):641–647, 1994. Rajinikanth, V., Fernandes, S.L., Bhushan, B., and Sunder, N.R., Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. Proceedings of 2nd international conference on micro-electronics, electromagnetics and telecommunications. Springer, 313–321, 2018. 1453_CR20 C Zhang (1453_CR17) 2018; 8 K Kamnitsas (1453_CR24) 2017; 36 1453_CR22 1453_CR27 BH Menze (1453_CR41) 2015; 34 1453_CR28 1453_CR26 1453_CR29 E Abdel-Maksoud (1453_CR23) 2015; 16 1453_CR6 S Naqi (1453_CR40) 2018; 14 1453_CR8 1453_CR7 1453_CR9 M Raza (1453_CR34) 2018; 88 O Maier (1453_CR43) 2017; 35 1453_CR50 1453_CR51 1453_CR2 1453_CR12 A Liaqat (1453_CR38) 2018; 18 1453_CR1 1453_CR13 X Zhao (1453_CR44) 2018; 43 1453_CR4 1453_CR10 1453_CR11 1453_CR16 1453_CR14 V Rajinikanth (1453_CR3) 2017; 94 1453_CR15 1453_CR18 1453_CR19 J Amin (1453_CR25) 2018; 87 SC Satapathy (1453_CR31) 2019; 14 1453_CR42 1453_CR45 1453_CR46 GJ Ansari (1453_CR39) 2018; 87 1453_CR49 J Amin (1453_CR35) 2017; 19 1453_CR48 M Havaei (1453_CR21) 2017; 35 N Nida (1453_CR5) 2016; 7 1453_CR30 1453_CR32 1453_CR33 1453_CR36 1453_CR37 S Chen (1453_CR47) 2019; 88
References_xml	– reference: Deng, W., Xiao, W., Deng, H., and Liu, J., MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve. Biomedical engineering and informatics (BMEI), 2010 3rd international conference on, IEEE. 393–396, 2010. – reference: Kamnitsas, K., Ferrante, E., Parisot, S., Ledig, C., Nori, A. V., Criminisi, A., Rueckert, D., and Glocker, B., DeepMedic for brain tumor segmentation. In: International workshop on Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer, 2016, 138–149. – reference: Zhang, L., Han, Y., Yang, Y., Song, M., Yan, S., and Tian, QJIToIP., Discovering discriminative graphlets for aerial image categories recognition. 22 (12):5071–5084, 2013. – reference: ChenSDingCLiuMDual-force convolutional neural networks for accurate brain tumor segmentationPattern Recogn.2019889010010.1016/j.patcog.2018.11.009 – reference: Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., and Büchler, P., The virtual skeleton database: An open access repository for biomedical research and collaboration. Journal of medical Internet research 15 (11), 2013. – reference: SatapathySCFernandesSLLinHStroke lesion segmentation and analysis using entropy/Otsu’s function–a study with social group optimizationCurr. Bioinform.20191443053131:CAS:528:DC%2BC1MXhtVGhsb3J10.2174/1574893614666181220094918 – reference: Zhou, B., Khosla, A., Lapedriza, A., Torralba, A., and Oliva, A., Places: An image database for deep scene understanding, (2016). – reference: Simon Andermatt, S.P., and Cattin, P., Multi-dimensional gated recurrent units for brain tumor segmentation. Proceedings of the 6th MICCAI BraTS Challenge (2017), 1984. – reference: Amorim, P.H.A.C.V.S., Escudero, G.G., Oliveira, D.D.C., Pereira, S.M., Santos, H.M., and Scussel, A.A., 3D U-nets for brain tumor segmentation in MICCAI 2017 BraTS challenge proceedings of the 6th MICCAI BraTS Challenge, 2017. – reference: Raja, N.S.M., Fernandes, S., Dey, N., Satapathy, S.C., and Rajinikanth, V., Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. Journal of Ambient Intelligence and Humanized Computing:1–12, 2018. – reference: Sharif, M., Khan, M.A., Faisal, M., Yasmin, M., and Fernandes, S.L., A framework for offline signature verification system: Best features selection approach. Pattern Recognition Letters, 2018. – reference: Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P.F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., and Wirkert, S., Nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:180910486, 2018. – reference: ZhaoXWuYSongGLiZZhangYFanYA deep learning model integrating FCNNs and CRFs for brain tumor segmentationMed. Image Analy.2018439811110.1016/j.media.2017.10.002 – reference: AnsariGJShahJHYasminMSharifMFernandesSLA novel machine learning approach for scene text extractionFut. Gen. Comput. Syst.20188732834010.1016/j.future.2018.04.074 – reference: Van Der Kouwe, A., Brain tumor segmentation from multi modal MR images using fully convolutional neural network. Proceedings of the 6th MICCAI BraTS challenge, 2017. – reference: Cui, Z., Yang, J., and Qiao, Y., Brain MRI segmentation with patch-based CNN approach. Control conference (CCC), 2016 35th Chinese. IEEE. 7026–7031, 2016. – reference: Han, J., Quan, R., Zhang, D., and Nie, F.J.I., ToIP Robust object co-segmentation using background prior. 27 (4):1639–1651, 2018. – reference: RazaMSharifMYasminMKhanMASabaTFernandesSLAppearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learningFut. Gen. Comput. Syst.201888283910.1016/j.future.2018.05.002 – reference: Zhang, L., Song, M., Liu, X., Bu, J., and Chen, C.J.S.P., Fast multi-view segment graph kernel for object classification. 93 (6):1597–1607, 2013. – reference: Hai, J., Qiao, K., Chen, J., Tan, H., Xu, J., Zeng, L., Shi, D., and Yan, B., Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation. Journal of Healthcare Engineering, 2019. – reference: LiaqatAKhanMAShahJHSharifMYasminMFernandesSLAutomated ulcer and bleeding classification from WCE images using multiple features fusion and selectionJ. Mech. Med. Biol.20181804185003810.1142/S0219519418500380 – reference: Ellwaa, A., Hussein, A., AlNaggar, E., Zidan, M., Zaki, M., Ismail, M.A., and Ghanem, N.M., Brain tumor segmantation using random forest trained on iteratively selected patients. International workshop on Brainlesion: Glioma, multiple sclerosis, stroke and traumatic brain injuries. Springer, 129–137, 2016. – reference: Zhao, L., and Jia K., Multiscale cnns for brain tumor segmentation and diagnosis. Computational and mathematical methods in medicine 2016. – reference: Lee, C.-H., Wang, S., Murtha, A., Brown, M.R., and Greiner, R., Segmenting brain tumors using pseudo–conditional random fields. International conference on medical image computing and computer-assisted intervention. Springer. 359–366, 2008. – reference: Upadhyay, N., and AJTBjor, W., Conventional MRI evaluation of gliomas. 84 (special_issue_2):S107-S111, 2011. – reference: RajinikanthVSatapathySCFernandesSLNachiappanSEntropy based segmentation of tumor from brain MR images–a study with teaching learning based optimizationPattern Recogn. Lett.201794879510.1016/j.patrec.2017.05.028 – reference: Alex, Krizhevsky., Sutskever, Ilya., and Hinton, GE., ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems, 2012. – reference: Ghosh, A., Maso, F.D., Roig, M., Mitsis, G.D., and Boudrias, M.-H., Deep semantic architecture with discriminative feature visualization for neuroimage analysis. arXiv preprint arXiv:180511704, 2018. – reference: NidaNSharifMKhanMUGYasminMFernandesSLA framework for automatic colorization of medical imagingIIOAB J.20167202209 – reference: AminJSharifMYasminMAliHFernandesSLA method for the detection and classification of diabetic retinopathy using structural predictors of bright lesionsJ. Comput. Sci.20171915316410.1016/j.jocs.2017.01.002 – reference: Abdel-MaksoudEElmogyMAl-AwadiRBrain tumor segmentation based on a hybrid clustering techniqueEgypt Inform. J.2015161718110.1016/j.eij.2015.01.003 – reference: AminJSharifMYasminMFernandesSLBig data analysis for brain tumor detection: Deep convolutional neural networksFut. Gen. Comput. Syst.20188729029710.1016/j.future.2018.04.065 – reference: Amin, J., Sharif, M., Yasmin, M., and Fernandes, S.L., A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognition Letters, 2017. – reference: Rajinikanth, V., Fernandes, S.L., Bhushan, B., and Sunder, N.R., Segmentation and analysis of brain tumor using Tsallis entropy and regularised level set. Proceedings of 2nd international conference on micro-electronics, electromagnetics and telecommunications. Springer, 313–321, 2018. – reference: NaqiSSharifMYasminMFernandesSLLung nodule detection using polygon approximation and hybrid features from CT imagesCurr. Med. Imag. Rev.201814110811710.2174/1573405613666170306114320 – reference: Yamashita, R., Nishio, M., Do, R.K.G., and Togashi, K., Convolutional neural networks: An overview and application in radiology. Insights into imaging:1–19, 2018. – reference: ZhangCFangMNieHBrain tumor segmentation using fully convolutional networks from magnetic resonance imagingJ. Med. Imag. Health Inform.2018881546155310.1166/jmihi.2018.2502 – reference: Gordillo, N., Montseny, E., and Sobrevilla, P.J., State of the art survey on MRI brain tumor segmentation. 31 (8):1426–1438, 2013. – reference: Dong, H., Yang, G., Liu, F., Mo, Y., Guo, Y., Automatic brain tumor detection and segmentation using U-net based fully convolutional networks. Annual conference on medical image understanding and analysis. Springer, 506–517, 2017. – reference: Cheng, G., Zhou, P., and Han, JJIToIP., Duplex metric learning for image set classification. 27 (1):281–292, 2018. – reference: Menze, B.H., Van Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., and Golland, P., A generative model for brain tumor segmentation in multi-modal images. International conference on medical image computing and computer-assisted intervention, Springer. 151–159, 2010. – reference: HavaeiMDavyAWarde-FarleyDBiardACourvilleABengioYPalCJodoinP-MLarochelleHBrain tumor segmentation with deep neural networksMed. Image Anal.201735183110.1016/j.media.2016.05.004 – reference: MenzeBHJakabABauerSKalpathy-CramerJFarahaniKKirbyJBurrenYPorzNSlotboomJWiestRThe multimodal brain tumor image segmentation benchmark (BRATS)IEEE Trans. Med. Imag.20153410199310.1109/TMI.2014.2377694 – reference: Bernal, J., Kushibar, K., Asfaw, D. S., Valverde, S., Oliver, A., Martí, R., and Lladó, X., Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: A review. Artificial intelligence in medicine, 2018. – reference: Reza, S.M., and Mays, R., Iftekharuddin KM multi-fractal detrended texture feature for brain tumor classification. Proceedings of SPIE--the International Society for Optical Engineering. NIH Public Access, 2015. – reference: Shah, J.H., Sharif, M., Yasmin, M., and Fernandes, S.L., Facial expressions classification and false label reduction using LDA and threefold SVM. Pattern Recognition Letters, 2017. – reference: KamnitsasKLedigCNewcombeVFSimpsonJPKaneADMenonDKRueckertDGlockerBEfficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentationMed. Image Analy.201736617810.1016/j.media.2016.10.004 – reference: MaierOMenzeBHvon der GablentzJHäniLHeinrichMPLiebrandMWinzeckSBasitABentleyPChenLISLES 2015-a public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRIMed. Image Analy.20173525026910.1016/j.media.2016.07.009 – reference: Bauer, S., Wiest, R., Nolte, L.-P., Reyes, M.J., PiM, Biology. A survey of MRI-based medical image analysis for brain tumor studies. 58 (13):R97, 2013. – reference: Bhagat, P., and Choudhary, P., Multiclass segmentation of brain tumor from MRI images. In: Applications of artificial intelligence techniques in engineering. Springer, 543–553, 2019. – reference: Adams, R., and Bischof, L.J., ITopa, intelligence m. Seeded region growing. 16 (6):641–647, 1994. – ident: 1453_CR49 – ident: 1453_CR15 doi: 10.1109/TIP.2017.2760512 – ident: 1453_CR13 doi: 10.1109/TIP.2013.2278465 – volume: 35 start-page: 250 year: 2017 ident: 1453_CR43 publication-title: Med. Image Analy. doi: 10.1016/j.media.2016.07.009 – ident: 1453_CR10 doi: 10.1007/s12652-018-0854-8 – ident: 1453_CR22 – volume: 88 start-page: 28 year: 2018 ident: 1453_CR34 publication-title: Fut. Gen. Comput. Syst. doi: 10.1016/j.future.2018.05.002 – volume: 87 start-page: 290 year: 2018 ident: 1453_CR25 publication-title: Fut. Gen. Comput. Syst. doi: 10.1016/j.future.2018.04.065 – ident: 1453_CR33 doi: 10.1167/17.10.296 – volume: 35 start-page: 18 year: 2017 ident: 1453_CR21 publication-title: Med. Image Anal. doi: 10.1016/j.media.2016.05.004 – ident: 1453_CR45 doi: 10.1007/978-981-13-1819-1_51 – ident: 1453_CR46 doi: 10.1117/12.2083596 – ident: 1453_CR26 doi: 10.1007/978-3-319-60964-5_44 – ident: 1453_CR36 doi: 10.1016/j.patrec.2017.06.021 – ident: 1453_CR32 – ident: 1453_CR51 – volume: 36 start-page: 61 year: 2017 ident: 1453_CR24 publication-title: Med. Image Analy. doi: 10.1016/j.media.2016.10.004 – ident: 1453_CR42 doi: 10.2196/jmir.2930 – ident: 1453_CR1 doi: 10.1016/j.patrec.2017.10.036 – volume: 7 start-page: 202 year: 2016 ident: 1453_CR5 publication-title: IIOAB J. – ident: 1453_CR7 doi: 10.1016/j.sigpro.2012.05.012 – ident: 1453_CR27 doi: 10.1007/978-3-319-55524-9_14 – ident: 1453_CR30 doi: 10.1155/2019/8415485 – volume: 19 start-page: 153 year: 2017 ident: 1453_CR35 publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2017.01.002 – ident: 1453_CR2 doi: 10.1088/0031-9155/58/13/R97 – ident: 1453_CR37 doi: 10.1016/j.patrec.2018.01.021 – volume: 16 start-page: 71 issue: 1 year: 2015 ident: 1453_CR23 publication-title: Egypt Inform. J. doi: 10.1016/j.eij.2015.01.003 – volume: 87 start-page: 328 year: 2018 ident: 1453_CR39 publication-title: Fut. Gen. Comput. Syst. doi: 10.1016/j.future.2018.04.074 – ident: 1453_CR50 – ident: 1453_CR19 doi: 10.1155/2016/8356294 – ident: 1453_CR28 doi: 10.1016/j.artmed.2018.08.008 – ident: 1453_CR12 doi: 10.1109/BMEI.2010.5639536 – ident: 1453_CR48 doi: 10.1007/978-3-319-55524-9_13 – ident: 1453_CR6 doi: 10.1016/j.mri.2013.05.002 – ident: 1453_CR9 doi: 10.1109/TIP.2017.2781424 – volume: 94 start-page: 87 year: 2017 ident: 1453_CR3 publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2017.05.028 – ident: 1453_CR4 doi: 10.1259/bjr/65711810 – ident: 1453_CR20 – ident: 1453_CR8 doi: 10.1109/34.295913 – volume: 8 start-page: 1546 issue: 8 year: 2018 ident: 1453_CR17 publication-title: J. Med. Imag. Health Inform. doi: 10.1166/jmihi.2018.2502 – volume: 14 start-page: 305 issue: 4 year: 2019 ident: 1453_CR31 publication-title: Curr. Bioinform. doi: 10.2174/1574893614666181220094918 – volume: 43 start-page: 98 year: 2018 ident: 1453_CR44 publication-title: Med. Image Analy. doi: 10.1016/j.media.2017.10.002 – ident: 1453_CR11 doi: 10.1007/978-981-10-4280-5_33 – ident: 1453_CR16 doi: 10.1007/978-3-540-85988-8_43 – volume: 18 start-page: 1850038 issue: 04 year: 2018 ident: 1453_CR38 publication-title: J. Mech. Med. Biol. doi: 10.1142/S0219519418500380 – ident: 1453_CR29 – volume: 34 start-page: 1993 issue: 10 year: 2015 ident: 1453_CR41 publication-title: IEEE Trans. Med. Imag. doi: 10.1109/TMI.2014.2377694 – volume: 88 start-page: 90 year: 2019 ident: 1453_CR47 publication-title: Pattern Recogn. doi: 10.1016/j.patcog.2018.11.009 – ident: 1453_CR14 doi: 10.1007/978-3-642-15745-5_19 – ident: 1453_CR18 – volume: 14 start-page: 108 issue: 1 year: 2018 ident: 1453_CR40 publication-title: Curr. Med. Imag. Rev. doi: 10.2174/1573405613666170306114320
SSID	ssj0009667
Score	2.5663083
Snippet	Brain tumor is one of the most death defying diseases nowadays. The tumor contains a cluster of abnormal cells grouped around the inner portion of human brain....
SourceID	proquest pubmed crossref springer
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	326
SubjectTerms	Algorithms Brain cancer Brain damage Brain Neoplasms - diagnostic imaging Brain Neoplasms - pathology Brain tumors Health Informatics Health Informatics and Computer Vision Health Sciences Humans Image & Signal Processing Image classification Image enhancement Image processing Image Processing, Computer-Assisted - methods Image segmentation Ischemia Magnetic Resonance Imaging - methods Medical imaging Medicine Medicine & Public Health Neural Networks, Computer Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Tomography, X-Ray Computed - methods Transfer learning Tumor cells Tumors
SummonAdditionalLinks	– databaseName: Computer Science Database dbid: K7- link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bi9UwEB50FRHEy3qrrhLBJyWYpmmaPslZ8SCoi7Ar7FvJrYvg9qzn4u93Jk3PQRb3xZcSml4CM5PM9RuA162XxLuaK2MlXmIkmSMUUe2aIGIZQkjNJpqjI3N62n7LDrdVTquc9sS0UYeFJx_5O0pYbqmbnHh_8YtT1yiKruYWGtfhRillSXz-ueE70F2tx3JpZTgBkU9RzbF0DhUjNKRbXqq64ubvc-mSsnkpUJrOn_m9_135fbibNU82G1nlAVyLwz7c-ppj6_twZ_TgsbEw6SEsZwx3QDbLoOMMtVt2SA0l2MnmHMfH8ew8Fy4NzA6Bpf6alHk03jrE8zEwHBwTUib7QtlJbL4h7xxLiQosnZN9XLIM8nr2CL7PP558-MRzhwbuq0auuWvRgA4uqlahoVaHPiijvat96VupTW0b64w1xrnYa2G1sbHSNuog6igU6o6PYW9YDPEpMOmtxGnVB1Q4ZAxWVaJ3sRIyCGe9LEBM9Ol8hi-nLho_ux3wMpG0Q5J2RNLOFPBm-8rFiN1x1cMHE_W6LMarbke6Al5tp1EAKapih7jYpGdM2aAaVBXwZGSW7d8qNEYJ0qyAtxP37D7-z6U8u3opz-G2THxLfqAD2FsvN_EF3PS_1z9Wy5dJAv4A8LQJqA priority: 102 providerName: ProQuest
Title	A New Approach for Brain Tumor Segmentation and Classification Based on Score Level Fusion Using Transfer Learning
URI	https://link.springer.com/article/10.1007/s10916-019-1453-8 https://www.ncbi.nlm.nih.gov/pubmed/31643004 https://www.proquest.com/docview/2307935190 https://www.proquest.com/docview/2308171523
Volume	43
WOSCitedRecordID	wos000492183400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLink customDbUrl: eissn: 1573-689X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009667 issn: 0148-5598 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3ra9RAEB9sKyJI1Vo1tR4r-EkJ7G2SzebjnfQQtMdxd5bDL2FfKYU2lXv49zuzSe6UqqBfhjw2D3ZndmZ3Zn4D8LawgnhXxqnSAon3JHOEIipN7rjvO-dCsYl8PFaLRTFp87hXXbR755IMM_VPyW5oyuDSt4j7aZbEag8OUNspksbp7GKHtCtlkyOdqpjQxztX5u9e8asyumNh3vGOBqUzevxfv_sEDlsbkw0apngK93x9BA_OWy_6ETxq9upYk4L0DJYDhnMdG7Tw4gztWDak0hFsvrnB45m_vGlTlGqma8dCJU2KMWouDVETOoYHM8LEZJ8pDomNNrQPx0JIAgsasfJL1sK5Xh7Dl9HZ_MPHuK3FENskF-vYFLhUdsanRYpLssxVLlXSmsz2bSGkynSujdJKGeMrybVU2idSe-l45nmKVuJz2K9va_8SmLBa4O20cmhaCO90mvDK-IQLx422IgLeDUppW6ByqpdxXe4glqlvS-zbkvq2VBG82z7yrUHp-Fvj026ky1ZgVyXFwxdUrJBH8GZ7G0WN_Ce69reb0Eb1czR4kgheNByy_VqCy04CL4vgfccOu5f_8VdO_qn1K3goAj_RBtAp7K-XG_8a7tvv66vVsgd7-fSC6CIPVPXgYHg2nkzx7FMeIz3nc6JiQjQnOsm-9oL0_AC1XQiC
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB5VBUElxKM8GihgJLiAIhwncZxDhbbAqlW3K6Qu0t5Sx3aqSjTb7gPEn-I3diZOdoUqeuuBS2TFTmI5n8cz9sw3AG9zIwi7MkyUFnhxjuYcsYjKMrPcRdbaJtlENhyq8Tj_tgZ_ulgYcqvsZGIjqO3E0B75R3JYzimbHP90fhFS1ig6Xe1SaHhYHLjfv9Bkm-3sf8H_-06I_tfR572wzSoQmjgT87DM0eizpUvyBI2L1FY2UdKUqYlMLqRKdaZLpZUqS1dJrqXSLpbaSctTx5OIyJdQ5N9COZ6RC1k2zlYkv1L68OxEhUR83p2i-lA9VMTQcM_DKEnjUP29Dl5Rbq8czDbrXf_B_zZSD-F-q1mznp8Kj2DN1Ztw57D1HdiEe36HkvnAq8cw7TGU8KzXkqoz1N7ZLiXMYKPFGZaP3MlZG5hVM11b1uQPJc8qf2sX13_LsHBETKBsQN5XrL-g3UfWOGKwRg-o3JS1JLYnT-D7jYzAU1ivJ7XbAiaMFlidVBYVKuGsTmJelS7mwvJSGxEA7_BQmJaenbKE_ChWxNIEoQIhVBCEChXA--Uj556b5LrG2x1ailZMzYoVVAJ4s6xGAUOnRrp2k0XTRkUZqnlxAM88OJdfi9HYJsq2AD50aF29_J9deX59V17D3b3R4aAY7A8PXsCGaOYM7Xltw_p8unAv4bb5OT-dTV81s4_B8U2D-BLoWWfa
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1ba9VAEB5KlSKIl3qLVl1BX5TQZLPZbB5ETq0HS-vhQCsUX9K9pQg2p56L4l_z1zmTTc5Bin3rgy9hyW6SZfPt7MzuzDcAL0vLCbsyFkpzvHhPc45YRKUpXOJT51ybbKIYjdTxcTleg999LAy5VfYysRXUbmJpj3ybHJZLyiaXbNedW8R4d_ju_HtMGaTopLVPpxEgsu9__UTzbfZ2bxf_9SvOhx-O3n-MuwwDsc0KPo9NiQagM16UAg2N3NVOKGlNblNbcqlyXWijtFLG-FomWirtM6m9dEnuE5ESEROK_2sF2phk-I3zLyvCXylDqLZQMZGg9yeqIWwPlTI04ss4FXkWq7_XxAuK7oVD2nbtG97-n0ftDtzqNG42CFPkLqz5ZhM2PnU-BZtwM-xcshCQdQ-mA4aSnw06snWGWj3boUQa7GhxhuVDf3rWBWw1TDeOtXlFyeMq3NpBvcAxLBwSQyg7IK8sNlzQriRrHTRYqx_Ufso6ctvT-_D5SkbgAaw3k8Y_Asat5lgtaoeKFvdOiyypjc8S7hKjLY8g6bFR2Y62nbKHfKtWhNMEpwrhVBGcKhXB6-Uj54Gz5LLGWz1yqk58zaoVbCJ4saxGwUOnSbrxk0XbRqUFqn9ZBA8DUJdfy9AIJyq3CN70yF29_J9deXx5V57DBmK3Otgb7T-BG7ydPrQVtgXr8-nCP4Xr9sf862z6rJ2IDE6uGsN_AOFBcM0
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+New+Approach+for+Brain+Tumor+Segmentation+and+Classification+Based+on+Score+Level+Fusion+Using+Transfer+Learning&rft.jtitle=Journal+of+medical+systems&rft.au=Amin%2C+Javeria&rft.au=Sharif%2C+Muhammad&rft.au=Yasmin%2C+Mussarat&rft.au=Saba%2C+Tanzila&rft.date=2019-11-01&rft.issn=1573-689X&rft.eissn=1573-689X&rft.volume=43&rft.issue=11&rft.spage=326&rft_id=info:doi/10.1007%2Fs10916-019-1453-8&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0148-5598&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0148-5598&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0148-5598&client=summon