{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T15:04:56Z","timestamp":1792076696030,"version":"4.4.0"},"reference-count":48,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,26]],"date-time":"2021-12-26T00:00:00Z","timestamp":1640476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006261","name":"Taif University","doi-asserted-by":"publisher","award":["TURSP-2020\/306"],"award-info":[{"award-number":["TURSP-2020\/306"]}],"id":[{"id":"10.13039\/501100006261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>In medical imaging, the detection and classification of stomach diseases are challenging due to the resemblance of different symptoms, image contrast, and complex background. Computer-aided diagnosis (CAD) plays a vital role in the medical imaging field, allowing accurate results to be obtained in minimal time. This article proposes a new hybrid method to detect and classify stomach diseases using endoscopy videos. The proposed methodology comprises seven significant steps: data acquisition, preprocessing of data, transfer learning of deep models, feature extraction, feature selection, hybridization, and classification. We selected two different CNN models (VGG19 and Alexnet) to extract features. We applied transfer learning techniques before using them as feature extractors. We used a genetic algorithm (GA) in feature selection, due to its adaptive nature. We fused selected features of both models using a serial-based approach. Finally, the best features were provided to multiple machine learning classifiers for detection and classification. The proposed approach was evaluated on a personally collected dataset of five classes, including gastritis, ulcer, esophagitis, bleeding, and healthy. We observed that the proposed technique performed superbly on Cubic SVM with 99.8% accuracy. For the authenticity of the proposed technique, we considered these statistical measures: classification accuracy, recall, precision, False Negative Rate (FNR), Area Under the Curve (AUC), and time. In addition, we provided a fair state-of-the-art comparison of our proposed technique with existing techniques that proves its worthiness.<\/jats:p>","DOI":"10.3390\/diagnostics12010043","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:00:54Z","timestamp":1640566854000},"page":"43","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos"],"prefix":"10.3390","volume":"12","author":[{"given":"M Shahbaz","family":"Ayyaz","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Ikram Ullah","family":"Lali","sequence":"additional","affiliation":[{"name":"Department of Information Sciences, University of Education Lahore, Lahore 41000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mubbashar","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1515-3187","authenticated-orcid":false,"given":"Hafiz Tayyab","family":"Rauf","sequence":"additional","affiliation":[{"name":"Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7759-0940","authenticated-orcid":false,"given":"Bader","family":"Alouffi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hashem","family":"Alyami","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahbaz","family":"Wasti","sequence":"additional","affiliation":[{"name":"Department of Information Sciences, University of Education Lahore, Lahore 41000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Dewi, A.K., Novianty, A., and Purboyo, T.W. (2016, January 28\u201329). Stomach disorder detection through the Iris Image using Backpropagation Neural Network. 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