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Syst."],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Wireless capsule endoscopy (WCE) enables imaging and diagnostics of the gastrointestinal (GI) tract to be performed without any discomfort. Despite this, several characteristics, including efficacy, tolerance, safety, and performance, make it difficult to apply and modify widely. The use of automated WCE to collect data and perform the analysis is essential for finding anomalies. Medical specialists need a significant amount of time and expertise to examine the data generated by WCE imaging of the patient\u2019s digestive tract. To address these challenges, several computer vision-based solutions have been designed; nevertheless, they do not achieve an acceptable level of accuracy, and more advancements are required. Thus, in this study, we proposed four multi-classification deep learning (DL) models i.e., Vgg-19\u2009+\u2009CNN, ResNet152V2, Gated Recurrent Unit (GRU)\u2009+\u2009ResNet152V2, and ResNet152V2\u2009+\u2009Bidirectional GRU (Bi-GRU) and applied it on different publicly available databases for diagnosing ulcerative colitis, polyps, and dyed-lifted polyps using WCE images. To our knowledge, this is the only study that uses a single DL model for the classification of three different GI diseases. We compared the classification performance of the proposed DL classifiers in terms of many parameters such as accuracy, loss, Matthew's correlation coefficient (MCC), recall, precision, negative predictive value (NPV), positive predictive value (PPV), and F1-score. The results revealed that the Vgg-19\u2009+\u2009CNN outperforms the three other proposed DL models in classifying GI diseases using WCE images. The Vgg-19\u2009+\u2009CNN model achieved an accuracy of 99.45%. The results of four proposed DL classifiers are also compared with recent state-of-the-art classifiers and the proposed Vgg-19\u2009+\u2009CNN model has performed better in terms of improved accuracy.<\/jats:p>","DOI":"10.1007\/s40747-023-01271-5","type":"journal-article","created":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T06:02:01Z","timestamp":1700805721000},"page":"2477-2497","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Multi-classification deep learning models for detection of ulcerative colitis, polyps, and dyed-lifted polyps using wireless capsule endoscopy images"],"prefix":"10.1007","volume":"10","author":[{"given":"Hassaan","family":"Malik","sequence":"first","affiliation":[]},{"given":"Ahmad","family":"Naeem","sequence":"additional","affiliation":[]},{"given":"Abolghasem","family":"Sadeghi-Niaraki","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7473-8441","authenticated-orcid":false,"given":"Rizwan Ali","family":"Naqvi","sequence":"additional","affiliation":[]},{"given":"Seung-Won","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,24]]},"reference":[{"key":"1271_CR1","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1097\/SLA.0b013e3181ca69a7","volume":"251","author":"SR Park","year":"2010","unstructured":"Park SR, Kim MJ, Ryu KW, Lee JH, Lee JS, Nam B-H et al (2010) Prognostic value of preoperative clinical staging assessed by computed tomography in resectable gastric cancer patients: a viewpoint in the era of preoperative treatment. 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