Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model
Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of cancer and patient-specific treatment options, the detection and classification of cancer becomes a difficult process. The manual recognition of...
Uloženo v:
| Vydáno v: | Healthcare (Basel) Ročník 10; číslo 6; s. 1040 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Switzerland
MDPI AG
02.06.2022
MDPI |
| Témata: | |
| ISSN: | 2227-9032, 2227-9032 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of cancer and patient-specific treatment options, the detection and classification of cancer becomes a difficult process. The manual recognition of osteosarcoma necessitates expert knowledge and is time consuming. An earlier identification of osteosarcoma can reduce the death rate. With the development of new technologies, automated detection models can be exploited for medical image classification, thereby decreasing the expert’s reliance and resulting in timely identification. In recent times, an amount of Computer-Aided Detection (CAD) systems are available in the literature for the segmentation and detection of osteosarcoma using medicinal images. In this view, this research work develops a wind driven optimization with deep transfer learning enabled osteosarcoma detection and classification (WDODTL-ODC) method. The presented WDODTL-ODC model intends to determine the presence of osteosarcoma in the biomedical images. To accomplish this, the osteosarcoma model involves Gaussian filtering (GF) based on pre-processing and contrast enhancement techniques. In addition, deep transfer learning using a SqueezNet model is utilized as a featured extractor. At last, the Wind Driven Optimization (WDO) algorithm with a deep-stacked sparse auto-encoder (DSSAE) is employed for the classification process. The simulation outcome demonstrated that the WDODTL-ODC technique outperformed the existing models in the detection of osteosarcoma on biomedical images. |
|---|---|
| AbstractList | Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of cancer and patient-specific treatment options, the detection and classification of cancer becomes a difficult process. The manual recognition of osteosarcoma necessitates expert knowledge and is time consuming. An earlier identification of osteosarcoma can reduce the death rate. With the development of new technologies, automated detection models can be exploited for medical image classification, thereby decreasing the expert's reliance and resulting in timely identification. In recent times, an amount of Computer-Aided Detection (CAD) systems are available in the literature for the segmentation and detection of osteosarcoma using medicinal images. In this view, this research work develops a wind driven optimization with deep transfer learning enabled osteosarcoma detection and classification (WDODTL-ODC) method. The presented WDODTL-ODC model intends to determine the presence of osteosarcoma in the biomedical images. To accomplish this, the osteosarcoma model involves Gaussian filtering (GF) based on pre-processing and contrast enhancement techniques. In addition, deep transfer learning using a SqueezNet model is utilized as a featured extractor. At last, the Wind Driven Optimization (WDO) algorithm with a deep-stacked sparse auto-encoder (DSSAE) is employed for the classification process. The simulation outcome demonstrated that the WDODTL-ODC technique outperformed the existing models in the detection of osteosarcoma on biomedical images.Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of cancer and patient-specific treatment options, the detection and classification of cancer becomes a difficult process. The manual recognition of osteosarcoma necessitates expert knowledge and is time consuming. An earlier identification of osteosarcoma can reduce the death rate. With the development of new technologies, automated detection models can be exploited for medical image classification, thereby decreasing the expert's reliance and resulting in timely identification. In recent times, an amount of Computer-Aided Detection (CAD) systems are available in the literature for the segmentation and detection of osteosarcoma using medicinal images. In this view, this research work develops a wind driven optimization with deep transfer learning enabled osteosarcoma detection and classification (WDODTL-ODC) method. The presented WDODTL-ODC model intends to determine the presence of osteosarcoma in the biomedical images. To accomplish this, the osteosarcoma model involves Gaussian filtering (GF) based on pre-processing and contrast enhancement techniques. In addition, deep transfer learning using a SqueezNet model is utilized as a featured extractor. At last, the Wind Driven Optimization (WDO) algorithm with a deep-stacked sparse auto-encoder (DSSAE) is employed for the classification process. The simulation outcome demonstrated that the WDODTL-ODC technique outperformed the existing models in the detection of osteosarcoma on biomedical images. Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of cancer and patient-specific treatment options, the detection and classification of cancer becomes a difficult process. The manual recognition of osteosarcoma necessitates expert knowledge and is time consuming. An earlier identification of osteosarcoma can reduce the death rate. With the development of new technologies, automated detection models can be exploited for medical image classification, thereby decreasing the expert's reliance and resulting in timely identification. In recent times, an amount of Computer-Aided Detection (CAD) systems are available in the literature for the segmentation and detection of osteosarcoma using medicinal images. In this view, this research work develops a wind driven optimization with deep transfer learning enabled osteosarcoma detection and classification (WDODTL-ODC) method. The presented WDODTL-ODC model intends to determine the presence of osteosarcoma in the biomedical images. To accomplish this, the osteosarcoma model involves Gaussian filtering (GF) based on pre-processing and contrast enhancement techniques. In addition, deep transfer learning using a SqueezNet model is utilized as a featured extractor. At last, the Wind Driven Optimization (WDO) algorithm with a deep-stacked sparse auto-encoder (DSSAE) is employed for the classification process. The simulation outcome demonstrated that the WDODTL-ODC technique outperformed the existing models in the detection of osteosarcoma on biomedical images. |
| Author | AL-Ghamdi, Abdullah S. AL-Malaise Ragab, Mahmoud Fakieh, Bahjat |
| AuthorAffiliation | 5 Department of Mathematics, Faculty of Science, Al-Azhar University, Naser City, Cairo 11884, Egypt 6 Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia 2 Information Systems Department, HECI School, Dar Alhekma University, Jeddah 22246, Saudi Arabia 3 Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia 1 Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; bfakieh@kau.edu.sa (B.F.); aalmalaise@kau.edu.sa (A.S.A.-M.A.-G.) 4 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| AuthorAffiliation_xml | – name: 5 Department of Mathematics, Faculty of Science, Al-Azhar University, Naser City, Cairo 11884, Egypt – name: 6 Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia – name: 3 Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia – name: 4 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia – name: 2 Information Systems Department, HECI School, Dar Alhekma University, Jeddah 22246, Saudi Arabia – name: 1 Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia; bfakieh@kau.edu.sa (B.F.); aalmalaise@kau.edu.sa (A.S.A.-M.A.-G.) |
| Author_xml | – sequence: 1 givenname: Bahjat orcidid: 0000-0003-2793-1238 surname: Fakieh fullname: Fakieh, Bahjat – sequence: 2 givenname: Abdullah S. AL-Malaise orcidid: 0000-0001-9259-4536 surname: AL-Ghamdi fullname: AL-Ghamdi, Abdullah S. AL-Malaise – sequence: 3 givenname: Mahmoud orcidid: 0000-0002-4427-0016 surname: Ragab fullname: Ragab, Mahmoud |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35742091$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kU1vFDEMhiNUREvpH-CARuLCZSEfszPJBaksn1LRHgpHFHkSD5symwxJBol_j2lLVYpELonj57Xs1w_ZQUwRGXss-HOlDH-xQ5jqzkFGwXkneMvvsSMpZb8yXMmDW-9DdlLKBadjhNJq_YAdqnXfSgqP2JftXMMepuY14tycV3Df0DfnM-SCzelSE0aXPObmFRRKbEvFVCC7tAeSVHQ1pNhA9M1mglLCGBxcfn0k1fSI3R9hKnhyfR-zz2_ffNq8X51t333YnJ6tXKt4XaGUI0A7aDS8G0fjTC-9BzFoIbVRXd9J1Oi96HzvJY6O63EQmmtowcHg1DF7eVV3XoY9eoexZpjsnGm0_NMmCPbvTAw7-zX9sIZcWouWCjy7LpDT9wVLtftQHE4TRExLsbLTgqw0ek3o0zvoRVpypPGI6qlzQnuintzu6KaVP84TIK8Al1MpGccbRHD7e8P23w2TSN8RuVAv_aapwvQ_6S_znK_y |
| CitedBy_id | crossref_primary_10_1080_0954898X_2024_2357660 crossref_primary_10_3389_fonc_2023_1207175 crossref_primary_10_1038_s41598_025_06784_2 crossref_primary_10_3390_cancers14246066 crossref_primary_10_3389_fmed_2025_1555907 crossref_primary_10_3390_diagnostics13020223 crossref_primary_10_3390_cancers15051492 |
| Cites_doi | 10.1038/s41374-021-00655-w 10.1145/3489088.3489093 10.1007/s42835-021-00859-6 10.1109/SPIN52536.2021.9566061 10.1155/2021/7433186 10.21528/CBIC2021-16 10.1039/D1AN01163D 10.1016/j.phrs.2021.105684 10.1109/ACCAI53970.2022.9752602 10.1002/int.22539 10.3390/s22030926 10.3390/ai3010011 10.1259/bjr.20201391 10.1016/j.eswa.2021.116003 10.1109/APS.2010.5562213 10.3390/jimaging8010002 10.1016/j.bspc.2022.103824 10.1016/j.bspc.2021.102931 10.3390/math10071090 10.1007/s11042-022-11949-6 10.4103/jpi.jpi_78_20 10.1007/s10723-021-09596-6 |
| ContentType | Journal Article |
| Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION NPM 3V. 7RV 7XB 8C1 8FI 8FJ 8FK 8G5 ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH GNUQQ GUQSH KB0 M2O MBDVC NAPCQ PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS Q9U 7X8 5PM |
| DOI | 10.3390/healthcare10061040 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) Nursing & Allied Health Database ProQuest Central (purchase pre-March 2016) Public Health Database ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Research Library (Alumni) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student Research Library Prep Nursing & Allied Health Database (Alumni Edition) Research Library Research Library (Corporate) Nursing & Allied Health Premium ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database Research Library Prep ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing Research Library (Alumni Edition) ProQuest Central China ProQuest Central Health Research Premium Collection ProQuest Central Korea Health & Medical Research Collection ProQuest Research Library ProQuest Central (New) ProQuest Public Health ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Nursing & Allied Health Source ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) Nursing & Allied Health Premium ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed CrossRef Publicly Available Content 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: 7RV name: Nursing & Allied Health Database url: https://search.proquest.com/nahs sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Public Health |
| EISSN | 2227-9032 |
| ExternalDocumentID | PMC9222514 35742091 10_3390_healthcare10061040 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: King Abdulaziz University grantid: G: 148-611-1441 – fundername: Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia grantid: G: 148-611-1441 |
| GroupedDBID | 53G 5VS 7RV 8C1 8FI 8FJ 8G5 AAFWJ AAHBH AAYXX ABUWG ADBBV AFFHD AFKRA ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BCNDV BENPR BPHCQ CCPQU CITATION DIK DWQXO FYUFA GNUQQ GUQSH GX1 HYE IAO IHR ITC KQ8 M2O M48 MODMG M~E NAPCQ OK1 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC RNS RPM UKHRP ALIPV NPM 3V. 7XB 8FK MBDVC PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c430t-e22faa4b8e906ff9c972dda1b8128936762e8edd16d7d2efc08fb1808a4acabc3 |
| IEDL.DBID | 7RV |
| ISICitedReferencesCount | 8 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000817449200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2227-9032 |
| IngestDate | Tue Nov 04 02:01:21 EST 2025 Sun Nov 09 11:07:48 EST 2025 Sat Jul 26 00:19:22 EDT 2025 Mon Jul 21 05:58:22 EDT 2025 Sat Nov 29 07:14:08 EST 2025 Tue Nov 18 21:03:50 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Keywords | image processing medical images computer aided diagnosis osteosarcoma deep transfer learning |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c430t-e22faa4b8e906ff9c972dda1b8128936762e8edd16d7d2efc08fb1808a4acabc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-9259-4536 0000-0002-4427-0016 0000-0003-2793-1238 |
| OpenAccessLink | https://www.proquest.com/docview/2679726817?pq-origsite=%requestingapplication% |
| PMID | 35742091 |
| PQID | 2679726817 |
| PQPubID | 2032390 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_9222514 proquest_miscellaneous_2681032985 proquest_journals_2679726817 pubmed_primary_35742091 crossref_primary_10_3390_healthcare10061040 crossref_citationtrail_10_3390_healthcare10061040 |
| PublicationCentury | 2000 |
| PublicationDate | 20220602 |
| PublicationDateYYYYMMDD | 2022-06-02 |
| PublicationDate_xml | – month: 6 year: 2022 text: 20220602 day: 2 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Healthcare (Basel) |
| PublicationTitleAlternate | Healthcare (Basel) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Huang (ref_19) 2022; 3 Sharma (ref_16) 2021; 2021 Chen (ref_8) 2021; 169 ref_14 Badashah (ref_5) 2021; 36 ref_12 ref_11 Ramli (ref_22) 2022; 17 ref_18 Pan (ref_7) 2022; 77 ref_17 ref_15 Wu (ref_10) 2022; 2022 Han (ref_2) 2021; 146 Anisuzzaman (ref_1) 2021; 69 Tang (ref_4) 2021; 12 ref_25 ref_23 Makielski (ref_3) 2021; 101 ref_21 Wang (ref_20) 2022; 20 Pereira (ref_9) 2021; 94 Barzekar (ref_13) 2022; 187 ref_6 Bansal (ref_24) 2022; 81 |
| References_xml | – volume: 101 start-page: 1585 year: 2021 ident: ref_3 article-title: Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning publication-title: Lab. Investig. doi: 10.1038/s41374-021-00655-w – ident: ref_15 doi: 10.1145/3489088.3489093 – volume: 17 start-page: 85 year: 2022 ident: ref_22 article-title: A Non-Convex Economic Dispatch Problem with Point-Valve Effect Using a Wind-Driven Optimisation Approach publication-title: J. Electr. Eng. Technol. doi: 10.1007/s42835-021-00859-6 – ident: ref_6 doi: 10.1109/SPIN52536.2021.9566061 – volume: 2021 start-page: 7433186 year: 2021 ident: ref_16 article-title: Bone Cancer Detection Using Feature Extraction Based Machine Learning Model publication-title: Comput. Math. Methods Med. doi: 10.1155/2021/7433186 – ident: ref_23 – ident: ref_14 doi: 10.21528/CBIC2021-16 – volume: 146 start-page: 6496 year: 2021 ident: ref_2 article-title: SERS and MALDI-TOF MS based plasma exosome profiling for rapid detection of osteosarcoma publication-title: Analyst doi: 10.1039/D1AN01163D – volume: 169 start-page: 105684 year: 2021 ident: ref_8 article-title: Advances in targeted therapy for osteosarcoma based on molecular classification publication-title: Pharmacol. Res. doi: 10.1016/j.phrs.2021.105684 – ident: ref_12 doi: 10.1109/ACCAI53970.2022.9752602 – volume: 36 start-page: 6007 year: 2021 ident: ref_5 article-title: Fractional-Harris hawks optimization-based generative adversarial network for osteosarcoma detection using Renyi entropy-hybrid fusion publication-title: Int. J. Intell. Syst. doi: 10.1002/int.22539 – ident: ref_18 doi: 10.3390/s22030926 – volume: 3 start-page: 180 year: 2022 ident: ref_19 article-title: Weight-Quantized SqueezeNet for Resource-Constrained Robot Vacuums for Indoor Obstacle Classification publication-title: AI doi: 10.3390/ai3010011 – volume: 94 start-page: 20201391 year: 2021 ident: ref_9 article-title: Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma publication-title: Br. J. Radiol. doi: 10.1259/bjr.20201391 – volume: 187 start-page: 116003 year: 2022 ident: ref_13 article-title: C-Net: A reliable convolutional neural network for biomedical image classification publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.116003 – ident: ref_21 doi: 10.1109/APS.2010.5562213 – ident: ref_25 doi: 10.3390/jimaging8010002 – volume: 77 start-page: 103824 year: 2022 ident: ref_7 article-title: Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma publication-title: Biomed. Signal Processing Control. doi: 10.1016/j.bspc.2022.103824 – volume: 69 start-page: 102931 year: 2021 ident: ref_1 article-title: A deep learning study on osteosarcoma detection from histological images publication-title: Biomed. Signal Processing Control. doi: 10.1016/j.bspc.2021.102931 – volume: 2022 start-page: 7703583 year: 2022 ident: ref_10 article-title: Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries publication-title: Comput. Math. Methods Med. – ident: ref_17 – ident: ref_11 doi: 10.3390/math10071090 – volume: 81 start-page: 8807 year: 2022 ident: ref_24 article-title: Automatic Detection of Osteosarcoma Based on Integrated Features and Feature Selection Using Binary Arithmetic Optimization Algorithm publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-022-11949-6 – volume: 12 start-page: 30 year: 2021 ident: ref_4 article-title: Improving generalization of deep learning models for diagnostic pathology by increasing variability in training data: Experiments on osteosarcoma subtypes publication-title: J. Pathol. Inform. doi: 10.4103/jpi.jpi_78_20 – volume: 20 start-page: 1 year: 2022 ident: ref_20 article-title: Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder publication-title: J. Grid Comput. doi: 10.1007/s10723-021-09596-6 |
| SSID | ssj0000913835 |
| Score | 2.2388728 |
| Snippet | Osteosarcoma is a kind of bone cancer which generally starts to develop in the lengthy bones in the legs and arms. Because of an increase in occurrence of... |
| SourceID | pubmedcentral proquest pubmed crossref |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 1040 |
| SubjectTerms | Algorithms Bone cancer Classification Histology Medical diagnosis Medical prognosis Sarcoma Semantics Tumors |
| Title | Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35742091 https://www.proquest.com/docview/2679726817 https://www.proquest.com/docview/2681032985 https://pubmed.ncbi.nlm.nih.gov/PMC9222514 |
| Volume | 10 |
| WOSCitedRecordID | wos000817449200001&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: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2227-9032 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913835 issn: 2227-9032 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 2227-9032 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913835 issn: 2227-9032 databaseCode: 7RV dateStart: 20130101 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2227-9032 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913835 issn: 2227-9032 databaseCode: BENPR dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 2227-9032 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913835 issn: 2227-9032 databaseCode: PIMPY dateStart: 20130101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: Public Health Database customDbUrl: eissn: 2227-9032 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913835 issn: 2227-9032 databaseCode: 8C1 dateStart: 20130101 isFulltext: true titleUrlDefault: https://search.proquest.com/publichealth providerName: ProQuest – providerCode: PRVPQU databaseName: Research Library customDbUrl: eissn: 2227-9032 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913835 issn: 2227-9032 databaseCode: M2O dateStart: 20130101 isFulltext: true titleUrlDefault: https://search.proquest.com/pqrl providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1La9wwEB7apIdCSfqumzSo0FsxsWTZkk8lT1podpf0wfZQjCzJJJDYm11vf39nbK-TNJBLLwKhh2Xmk-ah0QzAB-Q6LuGxDKVKfCgdwrjI4ihE1i4Tk5Q66kLmf1WjkZ5Os0lvcFv0bpWrM7E9qF1tyUa-K1KVKZFqrj7NrkLKGkW3q30KjYewzkk2Rjyr05-DjYViXqKE0b2ViVG73z0bnKo4Me-IbB43-dEdIfNfX8kbzOd483-X_RQ2erGT7XU4eQYPfPUcnnQ2O9Y9RXoBv8d4flxit0PvZwzFUNzhjn2boe7r2d6yqSnopfNzto-sz7ExAqRe4EapLw0OaVqnroqZyrE21SY5IbV0Z5Rw7eIl_Dg--n7wOezTL4RWxlETeiFKY2ShfRalZZlZ_BHnDC9QJkApJ8Vj1GvvHE-dcsKXNtJlwZG4RhprChu_grWqrvwbYMLy2BqpdFZmUkiFU6SIA4kzG-5lGQBfESG3fWxySpFxkaOOQoTL7xIugI_DmFkXmePe3tsr-uT9Ll3k18QJ4P3QjPuLLk1M5esl9dEUczDTSQCvOygMn4sTJQWCLAB1CyRDB4rdfbulOj9rY3hnpGdz-fb-ZW3BY0HPLcjqI7ZhrZkv_Tt4ZP8054v5Tgt2KqcaS33Ad2B9_2g0OcXaiRhjbfLlZPLrL-b4EmY |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLRJIiPcjUMBIcEJRY8eJkwNChVJ11e12JYrUHlBwbEet1CbLPkD8KX4jM3nRUqm3Hjjbntjx55nxeB4Ar1Hq2IiH0pcqcr60COM8DQMfRbuMdFQkQZMyf6TG4-TgIJ2swO8uFobcKjueWDNqWxmyka-LWKVKxAlX76fffaoaRa-rXQmNBhY77tdPvLLN3w03cX_fCLH1af_jtt9WFfCNDIOF74QotJZ54tIgLorUIFlrNc9R1KHwjpE7uMRZy2OrrHCFCZIi5zhnLbXRuQmR7jVYlQT2AaxOhruTw96qQ1k2UadponPCMA3Wj3o3Lk7qQkBWlrMS8IJa-6935hlxt3Xnf_tRd-F2q1izjeYk3IMVV96HW41VkjXBVg_g6x5yyFPstunclKGijTzMss9TvN07trFcVJTW07oZ-4DC3bI9PALVHJdSnWocsqjd1kqmS8vqYqLkZlUjm1FJuZOH8OVKVvgIBmVVuifAhOGh0VIlaZFKIRWSiBHpEilr7mThAe82PTNt9nUqAnKS4S2MgJJdBIoHb_sx0yb3yKW91zo8ZC0fmmd_weDBq74ZOQg9C-nSVUvqk1BWxTSJPHjcQK__XBgpKRDUHqhzoOw7UHby8y3l8VGdpTwlSwKXTy-f1ku4sb2_O8pGw_HOM7gpKLiEbFxiDQaL2dI9h-vmx-J4PnvRHjUG364atH8A7jxt3A |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Zb9QwEB6VLUJIiPtYKGAkeELRxo5z-AGhwrJi1bJdCZDKAwqO7aiV2mTZzYL4a_w6ZnLRUqlvfeDZ4yv5PDMezwHwHKWODXkgPRmHzpMWYZypwPdQtMtQh3niNynzd-PZLNnfV_MN-N3FwpBbZccTa0ZtS0M28pGIYhWLKOHxKG_dIubjyevFd48qSNFLa1dOo4HIjvv1E69vq1fTMf7rF0JM3n16-95rKwx4RgZ-5Tkhcq1lljjlR3muDE5hreYZij0U5BFyCpc4a3lkYytcbvwkzziuX0ttdGYCHPcSbKJKLsUANufTD_MvvYWHMm6iftNE6gSB8kcHvUsXJ9XBJ4vLSWl4RsX911PzhOib3PifP9pNuN4q3Gy7OSG3YMMVt-FaY61kTRDWHfi6h5zzGMnGzi0YKuDI2yz7uMBbv2Pb66qkdJ_WLdkbFPqW7eHRKFe4lfJYY5eqdmcrmC4sq4uMkvtVjXhGpeaO7sLnC9nhPRgUZeEeABOGB0bLOFG5kkLGOESEJ0DiyJo7mQ-BdwBITZuVnYqDHKV4OyPQpGdBM4SXfZ9Fk5PkXOqtDhtpy59W6V9gDOFZ34ychZ6LdOHKNdEklG1RJeEQ7jcw7KcLQkQ8AnwI8SmA9gSUtfx0S3F4UGcvV2Rh4PLh-ct6ClcQqenudLbzCK4Kijkh05fYgkG1XLvHcNn8qA5XyyftqWPw7aIx-wf83Hac |
| 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=Optimal+Deep+Stacked+Sparse+Autoencoder+Based+Osteosarcoma+Detection+and+Classification+Model&rft.jtitle=Healthcare+%28Basel%29&rft.au=Fakieh%2C+Bahjat&rft.au=Al-Ghamdi%2C+Abdullah+S+Al-Malaise&rft.au=Ragab%2C+Mahmoud&rft.date=2022-06-02&rft.issn=2227-9032&rft.eissn=2227-9032&rft.volume=10&rft.issue=6&rft_id=info:doi/10.3390%2Fhealthcare10061040&rft_id=info%3Apmid%2F35742091&rft.externalDocID=35742091 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2227-9032&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2227-9032&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2227-9032&client=summon |