{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T18:18:11Z","timestamp":1782929891362,"version":"3.54.5"},"reference-count":49,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T00:00:00Z","timestamp":1628467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The authors would like to thank the Deputy for Study and Innovation, Ministry of Education, Kingdom of Saudi Arabia, for funding this research through a grant (NU\/IFC\/INT\/01\/008) from the Najran University Institutional Funding Committee.","award":["NU\/IFC\/INT\/01\/008"],"award-info":[{"award-number":["NU\/IFC\/INT\/01\/008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The classification of whole slide images (WSIs) provides physicians with an accurate analysis of diseases and also helps them to treat patients effectively. The classification can be linked to further detailed analysis and diagnosis. Deep learning (DL) has made significant advances in the medical industry, including the use of magnetic resonance imaging (MRI) scans, computerized tomography (CT) scans, and electrocardiograms (ECGs) to detect life-threatening diseases, including heart disease, cancer, and brain tumors. However, more advancement in the field of pathology is needed, but the main hurdle causing the slow progress is the shortage of large-labeled datasets of histopathology images to train the models. The Kimia Path24 dataset was particularly created for the classification and retrieval of histopathology images. It contains 23,916 histopathology patches with 24 tissue texture classes. A transfer learning-based framework is proposed and evaluated on two famous DL models, Inception-V3 and VGG-16. To improve the productivity of Inception-V3 and VGG-16, we used their pre-trained weights and concatenated these with an image vector, which is used as input for the training of the same architecture. Experiments show that the proposed innovation improves the accuracy of both famous models. The patch-to-scan accuracy of VGG-16 is improved from 0.65 to 0.77, and for the Inception-V3, it is improved from 0.74 to 0.79.<\/jats:p>","DOI":"10.3390\/s21165361","type":"journal-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T05:17:06Z","timestamp":1628486226000},"page":"5361","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Transfer Learning Approach for Classification of Histopathology Whole Slide Images"],"prefix":"10.3390","volume":"21","author":[{"given":"Shakil","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Technology, University of Balochistan, Quetta 87300, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4806-6159","authenticated-orcid":false,"given":"Asadullah","family":"Shaikh","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8799-9448","authenticated-orcid":false,"given":"Hani","family":"Alshahrani","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5006-8527","authenticated-orcid":false,"given":"Abdullah","family":"Alghamdi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3624-9380","authenticated-orcid":false,"given":"Mesfer","family":"Alrizq","sequence":"additional","affiliation":[{"name":"College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junaid","family":"Baber","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, University of Balochistan, Quetta 87300, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maheen","family":"Bakhtyar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, University of Balochistan, Quetta 87300, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"264","DOI":"10.3389\/fmed.2019.00264","article-title":"Deep learning for whole slide image analysis: An overview","volume":"6","author":"Dimitriou","year":"2019","journal-title":"Front. 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