Stomach Deformities Recognition Using Rank-Based Deep Features Selection

Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient’s deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complet...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Journal of medical systems Ročník 43; číslo 12; s. 329
Hlavní autoři:	Khan, Muhammad Attique, Sharif, Muhammad, Akram, Tallha, Yasmin, Mussarat, Nayak, Ramesh Sunder
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.12.2019 Springer Nature B.V
Témata:	Abnormalities Artificial neural networks Capsule Endoscopy - methods Classification Colorectal cancer Computed tomography Computer simulation Endoscopy Entropy Feature extraction Feature recognition Health Informatics Health Informatics and Computer Vision Health Sciences Hemorrhage - diagnosis Hemorrhage - diagnostic imaging Humans Image & Signal Processing Image classification Image processing Image Processing, Computer-Assisted - methods Image segmentation Magnetic resonance imaging Medicine Medicine & Public Health Neural networks Neural Networks, Computer Optimization Physicians Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Stomach Diseases - diagnosis Stomach Diseases - diagnostic imaging Stomach Ulcer - diagnostic imaging Transfer learning WCE Deep features selection Saliency estimation Features fusion Colorectal cancer
ISSN:	0148-5598, 1573-689X, 1573-689X
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient’s deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur’s entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s.
AbstractList	Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient’s deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur’s entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s. Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur's entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s. Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur's entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s.Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur's entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s.
ArticleNumber	329
Author	Akram, Tallha Yasmin, Mussarat Khan, Muhammad Attique Nayak, Ramesh Sunder Sharif, Muhammad
Author_xml	– sequence: 1 givenname: Muhammad Attique surname: Khan fullname: Khan, Muhammad Attique organization: Department of CS&E, HITEC University – sequence: 2 givenname: Muhammad surname: Sharif fullname: Sharif, Muhammad email: muhammadsharifmalik@yahoo.com organization: Department of E&CE, COMSATS University Islamabad – sequence: 3 givenname: Tallha surname: Akram fullname: Akram, Tallha organization: Information Science, Canara Engineering College – sequence: 4 givenname: Mussarat surname: Yasmin fullname: Yasmin, Mussarat organization: Department of E&CE, COMSATS University Islamabad – sequence: 5 givenname: Ramesh Sunder surname: Nayak fullname: Nayak, Ramesh Sunder organization: Department of CS, COMSATS University Islamabad
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31676931$$D View this record in MEDLINE/PubMed
BookMark	eNp90c1O3DAUBWCrGlQGygN0gyJ1043BN8Z2sqT8S0hITEdiZ9mem2mGxB7sZNG3x8NAKyHByl58x766Z49MfPBIyHdgR8CYOk7AapCUQU3hRErKv5ApCMWprOqHCZkyOKmoEHW1S_ZSWjHGainVV7LLQSpZc5iS69kQeuP-FOfYhNi3Q4upuEcXlj7fgy_mqfXL4t74R_rLJFxkiOviEs0wxkxn2KHbwG9kpzFdwoPXc5_MLy9-n13T27urm7PTW-q4KgeqXNMAs2Ar1UgmrFyAM0ZUZelYgxWU1jmUC2s5B1GhE6quGiUtkyUaIS3fJz-3765jeBoxDbpvk8OuMx7DmHTJAaQApiDTH-_oKozR5-leFDAheJ3V4asabY8LvY5tb-Jf_bajDGALXAwpRWz-EWB604Pe9qBzD3rTg-Y5o95lXDuYzZ6GaNru02S5Tab8i19i_D_0x6FntC-aow
CitedBy_id	crossref_primary_10_1049_ipr2_13002 crossref_primary_10_1016_j_patrec_2019_11_034 crossref_primary_10_32604_cmc_2023_031890 crossref_primary_10_3390_s23167170 crossref_primary_10_1109_ACCESS_2023_3290997 crossref_primary_10_1080_01431161_2021_1954261 crossref_primary_10_1002_jemt_23578 crossref_primary_10_1038_s41598_021_83788_8 crossref_primary_10_1002_jemt_23447 crossref_primary_10_1016_j_imu_2024_101600 crossref_primary_10_1007_s00521_022_07445_5 crossref_primary_10_20517_2574_1225_2023_102 crossref_primary_10_1016_j_asoc_2023_110239 crossref_primary_10_1109_ACCESS_2020_3010448 crossref_primary_10_1109_ACCESS_2020_3034217 crossref_primary_10_1038_s41598_025_98400_6 crossref_primary_10_1080_23311916_2022_2142072 crossref_primary_10_1038_s41598_025_95002_0 crossref_primary_10_32604_cmc_2021_013191 crossref_primary_10_3390_s22072801 crossref_primary_10_32604_cmc_2021_013217 crossref_primary_10_1007_s11063_021_10481_2 crossref_primary_10_32604_cmc_2021_015916 crossref_primary_10_3390_app13159031 crossref_primary_10_1002_ima_22581 crossref_primary_10_1007_s40747_023_01271_5 crossref_primary_10_3390_diagnostics12112718 crossref_primary_10_1016_j_patrec_2019_12_006 crossref_primary_10_1111_den_13888 crossref_primary_10_1007_s11042_021_10714_5 crossref_primary_10_1063_5_0054161 crossref_primary_10_1016_j_patrec_2019_12_024 crossref_primary_10_32604_cmc_2021_014983 crossref_primary_10_3389_fsurg_2022_894775 crossref_primary_10_3892_ol_2022_13216 crossref_primary_10_1016_j_compbiomed_2023_106726 crossref_primary_10_1002_ima_22965 crossref_primary_10_1371_journal_pone_0302880 crossref_primary_10_32604_cmc_2023_045491 crossref_primary_10_1016_j_eswa_2020_114161 crossref_primary_10_32604_cmc_2021_017101 crossref_primary_10_1007_s00521_022_07895_x crossref_primary_10_1007_s10586_024_04601_5 crossref_primary_10_1007_s40747_021_00328_7 crossref_primary_10_1016_j_asoc_2019_105986 crossref_primary_10_32604_cmc_2023_036141
Cites_doi	10.1038/35013140 10.1166/jmihi.2017.2267 10.3322/caac.21387 10.1586/17474124.2014.952281 10.1007/s10916-019-1413-3 10.1142/S0219519419500556 10.1109/TASE.2016.2610579 10.1049/iet-ipr.2017.0368 10.1166/jmihi.2017.2265 10.1007/s11042-018-7031-0 10.1142/S0219519418500380 10.1007/s10916-014-0025-1 10.1109/TMI.2015.2418534 10.1002/jemt.23009 10.1007/s00521-017-2869-z 10.1109/JBHI.2013.2257819 10.1002/jemt.23238 10.1109/MCE.2019.2923926 10.1088/1361-6560/aad51c 10.1002/jemt.23301 10.1186/s12885-018-4465-8 10.1016/j.compag.2018.04.023 10.1016/j.bspc.2017.09.028 10.1002/jemt.23220 10.1016/j.future.2018.04.065 10.1002/jemt.23275 10.1166/jmihi.2017.2280 10.1007/s10916-019-1428-9 10.1007/s00521-016-2818-2 10.3390/app7101097 10.1109/CVPR.2016.90 10.1007/s11042-019-07875-9 10.1049/iet-its.2018.5069 10.1109/ICCV.2009.5459175 10.1145/3095713.3095731 10.1109/CVPR.2017.243 10.1080/0952813X.2019.1572657 10.1109/ATSIP.2017.8075590 10.1007/s12652-018-1051-5 10.1007/s10044-018-0688-1 10.1007/s00500-018-3424-2 10.1109/CVPR.2015.7298594 10.1007/s00521-019-04369-5 10.1109/TENCON.2017.8228058 10.1016/j.suscom.2019.08.002 10.1109/EMBC.2018.8513012 10.1007/s12652-018-1075-x 10.1109/ICCISci.2019.8716400 10.1007/978-981-13-1927-3_3 10.1007/s10586-017-1584-y
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/s10916-019-1466-3
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Aluminium Industry Abstracts Biotechnology Research Abstracts Ceramic Abstracts 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 Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest SciTech Premium Collection Technology Collection Advanced Technologies & Aerospace Collection ProQuest Central Essentials Biological Science Collection ProQuest Central Technology collection Natural Science Collection ProQuest One Community College 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 ProQuest Health & Medical Collection Healthcare Administration Database - QC 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	Materials Research Database MEDLINE MEDLINE - Academic
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	31676931 10_1007_s10916_019_1466_3
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-7cff10b1b87f605b6d1caa5822c0fe812bcce6dbb33158ec5798f76b062ea56b3
IEDL.DBID	K7-
ISICitedReferencesCount	59
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000493655300001&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 20:05:00 EDT 2025 Wed Nov 05 01:13:50 EST 2025 Wed Feb 19 02:30:43 EST 2025 Sat Nov 29 05:35:01 EST 2025 Tue Nov 18 22:09:26 EST 2025 Fri Feb 21 02:37:17 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	12
Keywords	WCE Deep features selection Saliency estimation Features fusion Colorectal cancer
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c372t-7cff10b1b87f605b6d1caa5822c0fe812bcce6dbb33158ec5798f76b062ea56b3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
PMID	31676931
PQID	2311105539
PQPubID	54050
ParticipantIDs	proquest_miscellaneous_2311651071 proquest_journals_2311105539 pubmed_primary_31676931 crossref_primary_10_1007_s10916_019_1466_3 crossref_citationtrail_10_1007_s10916_019_1466_3 springer_journals_10_1007_s10916_019_1466_3
PublicationCentury	2000
PublicationDate	2019-12-01
PublicationDateYYYYMMDD	2019-12-01
PublicationDate_xml	– month: 12 year: 2019 text: 2019-12-01 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	Fan, Xu, Fan, Wei, Li (CR43) 2018; 63 Liaqat, Khan, Shah, Sharif, Yasmin, Fernandes (CR5) 2018; 18 CR39 CR37 CR36 CR35 CR33 CR32 Fernandes, Gurupur, Lin, Martis (CR22) 2017; 7 Rajinikanth, Madhavaraja, Satapathy, Fernandes (CR27) 2017; 7 Chatterjee, Dey, Munshi (CR12) 2018; 40 Sharif, Khan, Iqbal, Azam, Lali, Javed (CR8) 2018; 150 Yuan, Wang, Li, Meng (CR38) 2015; 34 CR9 CR49 CR48 CR46 CR45 CR44 CR42 CR40 Yuan, Li, Meng (CR61) 2017; 14 Amin, Sharif, Yasmin, Fernandes (CR30) 2018; 87 Simonyan, Zisserman (CR18) 2014; arXiv Raja, Rajinikanth, Fernandes, Satapathy (CR52) 2017; 7 Koulaouzidis, Iakovidis, Karargyris, Plevris (CR47) 2015; 9 Khan, Lali, Rehman, Ishaq, Sharif, Saba (CR31) 2019; 82 Adeel, Khan, Sharif, Azam, Shah, Umer, Wan (CR58) 2019; 24 CR19 Siegel, Miller, Jemal (CR1) 2017; 67 CR17 Yin, Yu, Sohn, Liu, Chandraker (CR50) 2018; arXiv Iddan, Meron, Glukhovsky, Swain (CR3) 2000; 405 Mergener (CR4) 2008; 4 CR13 CR57 Acharya, Fernandes, WeiKoh, Ciaccio, Fabell, Tanik (CR34) 2019; 43 CR10 CR54 CR53 Khan, Akram, Sharif, Saba, Javed, Lali (CR25) 2019; 82 Fu, Zhang, Mandal, Meng (CR2) 2014; 18 Afza, Khan, Sharif, Rehman (CR26) 2019; 82 Lavanya, Rani (CR55) 2011; 26 Faris, Hassonah, Ala’M, Mirjalili, Aljarah (CR14) 2018; 30 Khan, Nazir, Khan, Saba, Javed, Rehman (CR23) 2019; 82 Fernandes, Rajinikanth, Kadry (CR21) 2019; 8 Saba, Khan, Rehman, Marie-Sainte (CR24) 2019; 43 Rashid, Khan, Sharif, Raza, Sarfraz, Afza (CR51) 2019; 78 CR29 Khan, Sharif, Javed, Akram, Yasmin, Saba (CR56) 2017; 12 CR20 Nasir, Attique Khan, Sharif, Lali, Saba, Iqbal (CR6) 2018; 81 Suman, Hussin, Malik, Ho, Hilmi, Leow (CR59) 2017; 7 Kaur, Saini, Gupta (CR15) 2018; 29 Naz, Muhammad, Yasmin, Sharif, Shah, Fernandes (CR28) 2019; 19 CR60 Khan, Akram, Sharif, Shahzad, Aurangzeb, Alhussein (CR7) 2018; 18 Sainju, Bui, Wahid (CR41) 2014; 38 Nur, Tjandrasa (CR11) 2018; 1108 Iandola, Han, Moskewicz, Ashraf, Dally, Keutzer (CR16) 2016; arXiv N Raja (1466_CR52) 2017; 7 A Liaqat (1466_CR5) 2018; 18 UR Acharya (1466_CR34) 2019; 43 X Yin (1466_CR50) 2018; arXiv 1466_CR13 1466_CR57 1466_CR10 1466_CR54 RL Siegel (1466_CR1) 2017; 67 K Simonyan (1466_CR18) 2014; arXiv 1466_CR53 M Nasir (1466_CR6) 2018; 81 FN Iandola (1466_CR16) 2016; arXiv S Sainju (1466_CR41) 2014; 38 D Lavanya (1466_CR55) 2011; 26 Y Yuan (1466_CR61) 2017; 14 1466_CR19 1466_CR17 S Suman (1466_CR59) 2017; 7 1466_CR40 1466_CR9 1466_CR48 1466_CR45 1466_CR46 1466_CR44 1466_CR42 A Koulaouzidis (1466_CR47) 2015; 9 1466_CR49 M Sharif (1466_CR8) 2018; 150 T Saba (1466_CR24) 2019; 43 SL Fernandes (1466_CR21) 2019; 8 F Afza (1466_CR26) 2019; 82 SL Fernandes (1466_CR22) 2017; 7 1466_CR36 1466_CR37 Y Fu (1466_CR2) 2014; 18 1466_CR35 1466_CR32 1466_CR33 SA Khan (1466_CR23) 2019; 82 S Fan (1466_CR43) 2018; 63 Alishba Adeel (1466_CR58) 2019; 24 1466_CR39 MA Khan (1466_CR56) 2017; 12 V Rajinikanth (1466_CR27) 2017; 7 M Rashid (1466_CR51) 2019; 78 1466_CR60 Y Yuan (1466_CR38) 2015; 34 K Mergener (1466_CR4) 2008; 4 MA Khan (1466_CR7) 2018; 18 N Nur (1466_CR11) 2018; 1108 S Chatterjee (1466_CR12) 2018; 40 1466_CR20 MA Khan (1466_CR31) 2019; 82 1466_CR29 J Amin (1466_CR30) 2018; 87 H Faris (1466_CR14) 2018; 30 T Kaur (1466_CR15) 2018; 29 G Iddan (1466_CR3) 2000; 405 MA Khan (1466_CR25) 2019; 82 I Naz (1466_CR28) 2019; 19
References_xml	– ident: CR45 – volume: 24 start-page: 100349 year: 2019 ident: CR58 article-title: Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion publication-title: Sustainable Computing: Informatics and Systems – volume: 405 start-page: 417 year: 2000 ident: CR3 article-title: Wireless capsule endoscopy publication-title: Nature doi: 10.1038/35013140 – volume: 7 start-page: 1825 year: 2017 end-page: 1829 ident: CR52 article-title: Segmentation of breast thermal images using Kapur's entropy and hidden Markov random field publication-title: Journal of Medical Imaging and Health Informatics doi: 10.1166/jmihi.2017.2267 – ident: CR49 – ident: CR39 – volume: 67 start-page: 7 year: 2017 end-page: 30 ident: CR1 article-title: Cancer statistics, 2017 publication-title: CA Cancer J. Clin. doi: 10.3322/caac.21387 – volume: 9 start-page: 217 year: 2015 end-page: 235 ident: CR47 article-title: Optimizing lesion detection in small-bowel capsule endoscopy: From present problems to future solutions publication-title: Expert review of gastroenterology & hepatology doi: 10.1586/17474124.2014.952281 – volume: 43 start-page: 289 year: 2019 ident: CR24 article-title: Region extraction and classification of skin cancer: A heterogeneous framework of deep CNN features fusion and reduction publication-title: J. Med. Syst. doi: 10.1007/s10916-019-1413-3 – volume: 4 start-page: 107 year: 2008 ident: CR4 article-title: Update on the use of capsule endoscopy publication-title: Gastroenterol. Hepatol. – volume: 1108 start-page: 012110 year: 2018 ident: CR11 article-title: Exudate Segmentation in Retinal Images of Diabetic Retinopathy Using Saliency Method Based on Region publication-title: Journal of Physics: Conference Series – volume: 19 year: 2019 ident: CR28 article-title: Robust discrimination of leukocytes protuberant types for early diagnosis of leukemia publication-title: Journal of Mechanics in Medicine and Biology doi: 10.1142/S0219519419500556 – volume: 14 start-page: 149 year: 2017 end-page: 159 ident: CR61 article-title: WCE abnormality detection based on saliency and adaptive locality-constrained linear coding publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2016.2610579 – ident: CR35 – ident: CR29 – ident: CR54 – volume: arXiv start-page: 1409.1556 year: 2014 ident: CR18 article-title: Very deep convolutional networks for large-scale image recognition publication-title: arXiv preprint – ident: CR42 – ident: CR46 – ident: CR19 – volume: 12 start-page: 200 year: 2017 end-page: 209 ident: CR56 article-title: License number plate recognition system using entropy-based features selection approach with SVM publication-title: IET Image Process. doi: 10.1049/iet-ipr.2017.0368 – volume: 7 start-page: 1837 year: 2017 end-page: 1840 ident: CR27 article-title: Otsu's multi-thresholding and active contour snake model to segment dermoscopy images publication-title: Journal of Medical Imaging and Health Informatics doi: 10.1166/jmihi.2017.2265 – volume: 78 start-page: 15751 year: 2019 end-page: 15777 ident: CR51 article-title: Object detection and classification: A joint selection and fusion strategy of deep convolutional neural network and SIFT point features publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-018-7031-0 – ident: CR9 – ident: CR57 – volume: 18 start-page: 1850038 year: 2018 ident: CR5 article-title: Automated ulcer and bleeding classification from Wce images using multiple features fusion and selection publication-title: Journal of Mechanics in Medicine and Biology doi: 10.1142/S0219519418500380 – ident: CR32 – volume: 38 start-page: 25 year: 2014 ident: CR41 article-title: Automated bleeding detection in capsule endoscopy videos using statistical features and region growing publication-title: J. Med. Syst. doi: 10.1007/s10916-014-0025-1 – ident: CR60 – ident: CR36 – volume: 34 start-page: 2046 year: 2015 end-page: 2057 ident: CR38 article-title: Saliency based ulcer detection for wireless capsule endoscopy diagnosis publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2015.2418534 – volume: 81 start-page: 528 year: 2018 end-page: 543 ident: CR6 article-title: An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.23009 – volume: 29 start-page: 193 year: 2018 end-page: 206 ident: CR15 article-title: A novel feature selection method for brain tumor MR image classification based on the fisher criterion and parameter-free bat optimization publication-title: Neural Comput. & Applic. doi: 10.1007/s00521-017-2869-z – volume: 18 start-page: 636 year: 2014 end-page: 642 ident: CR2 article-title: Computer-aided bleeding detection in WCE video publication-title: IEEE journal of biomedical and health informatics doi: 10.1109/JBHI.2013.2257819 – volume: 82 start-page: 909 year: 2019 end-page: 922 ident: CR31 article-title: Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.23238 – ident: CR37 – ident: CR53 – volume: 8 start-page: 31 year: 2019 end-page: 36 ident: CR21 article-title: A hybrid framework to evaluate breast abnormality using infrared thermal images publication-title: IEEE Consumer Electronics Magazine doi: 10.1109/MCE.2019.2923926 – volume: arXiv start-page: 1803.09014 year: 2018 ident: CR50 article-title: Feature transfer learning for deep face recognition with long-tail data publication-title: arXiv preprint – ident: CR10 – ident: CR33 – volume: 26 start-page: 1 year: 2011 end-page: 4 ident: CR55 article-title: Performance evaluation of decision tree classifiers on medical datasets publication-title: Int. J. Comput. Appl. – volume: 63 year: 2018 ident: CR43 article-title: Computer-aided detection of small intestinal ulcer and erosion in wireless capsule endoscopy images publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/aad51c – ident: CR40 – volume: 82 start-page: 1471 year: 2019 end-page: 1488 ident: CR26 article-title: Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.23301 – volume: 18 start-page: 638 year: 2018 ident: CR7 article-title: An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification publication-title: BMC Cancer doi: 10.1186/s12885-018-4465-8 – ident: CR44 – ident: CR48 – volume: 150 start-page: 220 year: 2018 end-page: 234 ident: CR8 article-title: Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.04.023 – ident: CR17 – volume: 40 start-page: 252 year: 2018 end-page: 262 ident: CR12 article-title: Optimal selection of features using wavelet fractal descriptors and automatic correlation bias reduction for classifying skin lesions publication-title: Biomedical signal processing and control doi: 10.1016/j.bspc.2017.09.028 – ident: CR13 – volume: 82 start-page: 741 year: 2019 end-page: 763 ident: CR25 article-title: Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.23220 – volume: 87 start-page: 290 year: 2018 end-page: 297 ident: CR30 article-title: Big data analysis for brain tumor detection: Deep convolutional neural networks publication-title: Futur. Gener. Comput. Syst. doi: 10.1016/j.future.2018.04.065 – volume: 82 start-page: 1256 year: 2019 end-page: 1266 ident: CR23 article-title: Lungs nodule detection framework from computed tomography images using support vector machine publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.23275 – volume: 7 start-page: 1841 year: 2017 end-page: 1850 ident: CR22 article-title: A novel fusion approach for early lung cancer detection using computer aided diagnosis techniques publication-title: Journal of Medical Imaging and Health Informatics doi: 10.1166/jmihi.2017.2280 – volume: arXiv start-page: 1602.07360 year: 2016 ident: CR16 article-title: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size publication-title: arXiv preprint – volume: 43 start-page: 302 year: 2019 ident: CR34 article-title: Automated detection of Alzheimer’s disease using brain MRI images–a study with various feature extraction techniques publication-title: J. Med. Syst. doi: 10.1007/s10916-019-1428-9 – volume: 30 start-page: 2355 year: 2018 end-page: 2369 ident: CR14 article-title: A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture publication-title: Neural Comput. & Applic. doi: 10.1007/s00521-016-2818-2 – ident: CR20 – volume: 7 start-page: 1097 year: 2017 ident: CR59 article-title: Feature selection and classification of ulcerated lesions using statistical analysis for WCE images publication-title: Appl. Sci. doi: 10.3390/app7101097 – volume: 78 start-page: 15751 year: 2019 ident: 1466_CR51 publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-018-7031-0 – volume: 26 start-page: 1 year: 2011 ident: 1466_CR55 publication-title: Int. J. Comput. Appl. – volume: 9 start-page: 217 year: 2015 ident: 1466_CR47 publication-title: Expert review of gastroenterology & hepatology doi: 10.1586/17474124.2014.952281 – ident: 1466_CR46 – volume: 43 start-page: 289 year: 2019 ident: 1466_CR24 publication-title: J. Med. Syst. doi: 10.1007/s10916-019-1413-3 – ident: 1466_CR42 – ident: 1466_CR17 doi: 10.1109/CVPR.2016.90 – ident: 1466_CR32 doi: 10.1007/s11042-019-07875-9 – ident: 1466_CR13 – volume: 29 start-page: 193 year: 2018 ident: 1466_CR15 publication-title: Neural Comput. & Applic. doi: 10.1007/s00521-017-2869-z – volume: 82 start-page: 1471 year: 2019 ident: 1466_CR26 publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.23301 – volume: 7 start-page: 1097 year: 2017 ident: 1466_CR59 publication-title: Appl. Sci. doi: 10.3390/app7101097 – ident: 1466_CR57 doi: 10.1049/iet-its.2018.5069 – volume: 18 start-page: 636 year: 2014 ident: 1466_CR2 publication-title: IEEE journal of biomedical and health informatics doi: 10.1109/JBHI.2013.2257819 – volume: 7 start-page: 1825 year: 2017 ident: 1466_CR52 publication-title: Journal of Medical Imaging and Health Informatics doi: 10.1166/jmihi.2017.2267 – ident: 1466_CR48 doi: 10.1109/ICCV.2009.5459175 – volume: 12 start-page: 200 year: 2017 ident: 1466_CR56 publication-title: IET Image Process. doi: 10.1049/iet-ipr.2017.0368 – volume: 7 start-page: 1837 year: 2017 ident: 1466_CR27 publication-title: Journal of Medical Imaging and Health Informatics doi: 10.1166/jmihi.2017.2265 – volume: 82 start-page: 909 year: 2019 ident: 1466_CR31 publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.23238 – ident: 1466_CR40 doi: 10.1145/3095713.3095731 – ident: 1466_CR49 doi: 10.1109/CVPR.2017.243 – volume: 63 year: 2018 ident: 1466_CR43 publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/aad51c – ident: 1466_CR20 – ident: 1466_CR33 doi: 10.1080/0952813X.2019.1572657 – volume: 82 start-page: 741 year: 2019 ident: 1466_CR25 publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.23220 – volume: 30 start-page: 2355 year: 2018 ident: 1466_CR14 publication-title: Neural Comput. & Applic. doi: 10.1007/s00521-016-2818-2 – volume: 1108 start-page: 012110 year: 2018 ident: 1466_CR11 publication-title: Journal of Physics: Conference Series – ident: 1466_CR39 doi: 10.1109/ATSIP.2017.8075590 – ident: 1466_CR10 doi: 10.1007/s12652-018-1051-5 – ident: 1466_CR54 doi: 10.1007/s10044-018-0688-1 – volume: 19 year: 2019 ident: 1466_CR28 publication-title: Journal of Mechanics in Medicine and Biology doi: 10.1142/S0219519419500556 – volume: 18 start-page: 638 year: 2018 ident: 1466_CR7 publication-title: BMC Cancer doi: 10.1186/s12885-018-4465-8 – ident: 1466_CR53 doi: 10.1007/s00500-018-3424-2 – volume: 8 start-page: 31 year: 2019 ident: 1466_CR21 publication-title: IEEE Consumer Electronics Magazine doi: 10.1109/MCE.2019.2923926 – volume: 82 start-page: 1256 year: 2019 ident: 1466_CR23 publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.23275 – volume: 14 start-page: 149 year: 2017 ident: 1466_CR61 publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2016.2610579 – volume: 405 start-page: 417 year: 2000 ident: 1466_CR3 publication-title: Nature doi: 10.1038/35013140 – ident: 1466_CR19 doi: 10.1109/CVPR.2015.7298594 – ident: 1466_CR44 – ident: 1466_CR29 doi: 10.1007/s00521-019-04369-5 – volume: 150 start-page: 220 year: 2018 ident: 1466_CR8 publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.04.023 – volume: 38 start-page: 25 year: 2014 ident: 1466_CR41 publication-title: J. Med. Syst. doi: 10.1007/s10916-014-0025-1 – volume: arXiv start-page: 1803.09014 year: 2018 ident: 1466_CR50 publication-title: arXiv preprint – volume: 7 start-page: 1841 year: 2017 ident: 1466_CR22 publication-title: Journal of Medical Imaging and Health Informatics doi: 10.1166/jmihi.2017.2280 – ident: 1466_CR60 doi: 10.1109/TENCON.2017.8228058 – volume: 81 start-page: 528 year: 2018 ident: 1466_CR6 publication-title: Microsc. Res. Tech. doi: 10.1002/jemt.23009 – volume: 4 start-page: 107 year: 2008 ident: 1466_CR4 publication-title: Gastroenterol. Hepatol. – volume: arXiv start-page: 1602.07360 year: 2016 ident: 1466_CR16 publication-title: arXiv preprint – volume: 18 start-page: 1850038 year: 2018 ident: 1466_CR5 publication-title: Journal of Mechanics in Medicine and Biology doi: 10.1142/S0219519418500380 – volume: 24 start-page: 100349 year: 2019 ident: 1466_CR58 publication-title: Sustainable Computing: Informatics and Systems doi: 10.1016/j.suscom.2019.08.002 – volume: 87 start-page: 290 year: 2018 ident: 1466_CR30 publication-title: Futur. Gener. Comput. Syst. doi: 10.1016/j.future.2018.04.065 – volume: 43 start-page: 302 year: 2019 ident: 1466_CR34 publication-title: J. Med. Syst. doi: 10.1007/s10916-019-1428-9 – volume: 67 start-page: 7 year: 2017 ident: 1466_CR1 publication-title: CA Cancer J. Clin. doi: 10.3322/caac.21387 – volume: arXiv start-page: 1409.1556 year: 2014 ident: 1466_CR18 publication-title: arXiv preprint – ident: 1466_CR45 doi: 10.1109/EMBC.2018.8513012 – ident: 1466_CR9 doi: 10.1007/s12652-018-1075-x – ident: 1466_CR36 doi: 10.1109/ICCISci.2019.8716400 – volume: 40 start-page: 252 year: 2018 ident: 1466_CR12 publication-title: Biomedical signal processing and control doi: 10.1016/j.bspc.2017.09.028 – volume: 34 start-page: 2046 year: 2015 ident: 1466_CR38 publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2015.2418534 – ident: 1466_CR35 doi: 10.1007/978-981-13-1927-3_3 – ident: 1466_CR37 doi: 10.1007/s10586-017-1584-y
SSID	ssj0009667
Score	2.4535692
Snippet	Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient’s deformity during the review time. Among set of... Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of...
SourceID	proquest pubmed crossref springer
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	329
SubjectTerms	Abnormalities Artificial neural networks Capsule Endoscopy - methods Classification Colorectal cancer Computed tomography Computer simulation Endoscopy Entropy Feature extraction Feature recognition Health Informatics Health Informatics and Computer Vision Health Sciences Hemorrhage - diagnosis Hemorrhage - diagnostic imaging Humans Image & Signal Processing Image classification Image processing Image Processing, Computer-Assisted - methods Image segmentation Magnetic resonance imaging Medicine Medicine & Public Health Neural networks Neural Networks, Computer Optimization Physicians Recent Advances in Deep Learning for Biomedical Signal Processing Statistics for Life Sciences Stomach Diseases - diagnosis Stomach Diseases - diagnostic imaging Stomach Ulcer - diagnostic imaging Transfer learning
SummonAdditionalLinks	– databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1ZS8QwEB50FRHE-6gXEXxSAu2mTZtHT_ZBF9lV8a00R1HUdtnD3--kx67iAfraTNOQyWS-6VwAh9rlKkmVptowTn0eaSo0MoSxpvSEVjoq6hbcX4XtdvTwIG6qPO5BHe1euySLm_pDshtCGTR9BUXp5pRNwwxqu8hKY6d7P6m0y3mZI-1H1FYfr12Z303xWRl9QZhfvKOF0rlc-tdyl2GxwpjkpDwUKzBlslWYu6686KuwUP6rI2UK0hq0usP8NVGP5NxYCFvUWCWdOrIoz0gRV0A6SfZMT1HraSQ0PWLR4witddIteukg4TrcXV7cnrVo1WGBKhY2hzRUaeq50pNRmKJdI7n2VJIECBqUmxrU_VIpw7WUjHlBZFQQiigNuXR50yQBl2wDGlmemS0giEN8fI7zIUDxAiV9JVyf-doClFQkDrj1VseqKj9uu2C8xJPCyXbHYtwxa4_wmDlwNH6lV9be-I14t-ZfXInhIEbw6tkOoEw4cDAeRgGyXpEkM_mopOF4M4WeA5sl38dfs2UCuGA4clwzeTL5j0vZ_hP1Dsw3i1NiQ2R2oTHsj8wezKq34dOgv1-c7XdGw_Bh priority: 102 providerName: Springer Nature
Title	Stomach Deformities Recognition Using Rank-Based Deep Features Selection
URI	https://link.springer.com/article/10.1007/s10916-019-1466-3 https://www.ncbi.nlm.nih.gov/pubmed/31676931 https://www.proquest.com/docview/2311105539 https://www.proquest.com/docview/2311651071
Volume	43
WOSCitedRecordID	wos000493655300001&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 Contemporary 1997-Present 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/eLvHCXMwpV3db9MwED-xDaFJaMCAURhVkHgCWYvjxE6e0AabJsGqKh1VxUsUf0SgQdKtLX8_d07SCk3shZd7iC-OlTuff-ez7wDe2lCasjKWWScki2VqWWZRIEJEmmfW2NTnLZh-UaNROptl427DbdEdq-xtojfUtjG0R36EOIRTMUeRfZhfM6oaRdHVroTGFuzwKOKk558V2yTdlbK9Lh2njBKR91HN9uocAiN0pDOGtkIy8fe6dAts3gqU-vXn7NH_jvwx7HXIMzhuVeUJ3HP1Pjy46GLr-_Cw3cEL2otJT-F8smx-leZ78MkRsPWZV4O8P2_U1IE_bRDkZX3FTnAttMjo5gFhyhX68MHEV9hBxmfw9ez08uM56-ouMCNUtGTKVBUPNdepqtDb0dJyU5YJQgkTVg4RgTbGSau1EDxJnUlUllZK6lBGrkykFs9hu25q9wICRCcxPsf-ELbwxOjYZGEsYkuwpcrKAYT9Xy9Ml5ScamP8LDbplElQBQqKvBRZiAG8W78ybzNy3MV82Muk6CbnotgIZABv1s04rShWUtauWbU8Eu2V4gM4aFVg_TVKHiAzgS3ve53YdP7Poby8eyivYDfy2kgnZQ5he3mzcq_hvvm9_LG4GcKWyqdEZ8rTdAg7J6ejcT70Go_0IrwkGo2JKqLj5BvSfDL9A4oJA-o
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgigS4lEKXVrASHABWU3ixI4PVQWUaqtuV6gPtLc0fkRUbZPtPkD8KX5jx3GyK1TRWw9c44nz8OeZz57xDMA7E3CdF9pQYxmnMU8NlQYHhLFIhdJok9Z5C773RL-fDgby2wL8ac_CuLDKVifWitpU2u2RbyAPCV0xRya3hpfUVY1y3tW2hIaHxZ79_QuXbOPN3W0c3_dRtPP16EuXNlUFqGYimlChiyIMVKhSUSCXV9yEOs8TNJQ6KCzaO6W15UYpxsIktToRMi0EVwGPbJ5wxbDfO3AX9bhwIWRiIOZJfjn3x7PjlLrE560X1R_VQyKGC3dJUTdxyv62g9fI7TXHbG3vdh7_b3_qCTxqmDX55KfCU1iw5TLc329iB5bhod-hJP7g1TPoHk6qi1z_INvWEfc6syw5aOOpqpLU0RTkIC_P6Ge09QYF7ZA4zjwdoehhXUEIBVfg-Fa-6zksllVpV4Eg-4rxOvaHtCxMtIq1DGIWG0fLCpl3IGhHOdNN0nVX--M8m6eLdsDIEBhuFcYz1oEPs1uGPuPITcLrLQayRvmMszkAOvB21oxqw_mC8tJWUy_DUR-LsAMvPORmT3PJEbhk2PKxxeC883--ysubX-UNLHWP9ntZb7e_twYPonomuKigdVicjKb2FdzTPyen49Hrek4ROLltaF4B_MxZjw
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LT9wwEB4tW4SQKlqgj6VQXKlcqCySOHHiQ1W1bFcg6GrFo0K9pPEjooIm23206l_rr-s4TnZVoXLjwDWeOA9_Hn_jGc8AvNYeV1muNNWGcRryRFOhcUAYC6QvtNJJlbfg83Hc7ycXF2LQgj_NWRgbVtnoxEpR61LZPfI95CG-LebIxF5eh0UMur13wx_UVpCyntamnIaDyJH5_QvNt_Hbwy6O9U4Q9D6e7R_QusIAVSwOJjRWee570pdJnCOvl1z7KssiXDSVlxtc-6RShmspGfOjxKgoFkkec-nxwGQRlwz7XYAHMdqY1vAbRF_mCX85d0e1w4TaJOiNR9Ud20NShka8oKinOGX_rok3iO4NJ2219vUe3ee_9hhWasZN3rspsgotU6zB0qc6pmANHrqdS-IOZK3Dwemk_J6pS9I1ltBXGWfJSRNnVRakirIgJ1lxRT8gB9AoaIbEcunpCEVPq8pCKPgEzu_ku55CuygL8xwIsrIQr2N_SNf8SMlQCS9kobZ0LRdZB7xmxFNVJ2O3NUGu03kaaQuSFEFirTOesg7szm4ZukwktwlvNnhIa6U0Tudg6MCrWTOqE-sjygpTTp0MRz0d-x145uA3e5pNmsAFw5Y3DR7nnf_3VTZuf5VtWEJEpseH_aMXsBxUk8IGC21CezKami1YVD8n38ajl9X0IvD1rpH5F5UWYoI
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=Stomach+Deformities+Recognition+Using+Rank-Based+Deep+Features+Selection&rft.jtitle=Journal+of+medical+systems&rft.au=Khan%2C+Muhammad+Attique&rft.au=Sharif%2C+Muhammad&rft.au=Tallha%2C+Akram&rft.au=Mussarat%2C+Yasmin&rft.date=2019-12-01&rft.pub=Springer+Nature+B.V&rft.issn=0148-5598&rft.eissn=1573-689X&rft.volume=43&rft.issue=12&rft_id=info:doi/10.1007%2Fs10916-019-1466-3&rft.externalDBID=HAS_PDF_LINK
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