{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:07:53Z","timestamp":1774678073344,"version":"3.50.1"},"reference-count":55,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T00:00:00Z","timestamp":1680480000000},"content-version":"vor","delay-in-days":92,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011821","name":"Ministry of Education \u2013 Kingdom of Saudi Arabi","doi-asserted-by":"publisher","award":["IFP-22 139"],"award-info":[{"award-number":["IFP-22 139"]}],"id":[{"id":"10.13039\/501100011821","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:p>Gastrointestinal (GI) diseases, particularly tumours, are considered one of the most widespread and dangerous diseases and thus need timely health care for early detection to reduce deaths. Endoscopy technology is an effective technique for diagnosing GI diseases, thus producing a video containing thousands of frames. However, it is difficult to analyse all the images by a gastroenterologist, and it takes a long time to keep track of all the frames. Thus, artificial intelligence systems provide solutions to this challenge by analysing thousands of images with high speed and effective accuracy. Hence, systems with different methodologies are developed in this work. The first methodology for diagnosing endoscopy images of GI diseases is by using VGG\u201016\u2009+\u2009SVM and DenseNet\u2010121\u2009+\u2009SVM. The second methodology for diagnosing endoscopy images of gastrointestinal diseases by artificial neural network (ANN) is based on fused features between VGG\u201016 and DenseNet\u2010121 before and after high\u2010dimensionality reduction by the principal component analysis (PCA). The third methodology is by ANN and is based on the fused features between VGG\u201016 and handcrafted features and features fused between DenseNet\u2010121 and the handcrafted features. Herein, handcrafted features combine the features of gray level cooccurrence matrix (GLCM), discrete wavelet transform (DWT), fuzzy colour histogram (FCH), and local binary pattern (LBP) methods. All systems achieved promising results for diagnosing endoscopy images of the gastroenterology data set. The ANN network reached an accuracy, sensitivity, precision, specificity, and an AUC of 98.9%, 98.70%, 98.94%, 99.69%, and 99.51%, respectively, based on fused features of the VGG\u201016 and the handcrafted.<\/jats:p>","DOI":"10.1155\/2023\/8616939","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T00:50:12Z","timestamp":1680569412000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features"],"prefix":"10.1155","volume":"2023","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6367-0309","authenticated-orcid":false,"given":"Zeyad","family":"Ghaleb Al-Mekhlafi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4635-929X","authenticated-orcid":false,"given":"Ebrahim","family":"Mohammed Senan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0619-0020","authenticated-orcid":false,"given":"Jalawi","family":"Sulaiman Alshudukhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3539-4161","authenticated-orcid":false,"given":"Badiea","family":"Abdulkarem Mohammed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,4,3]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-4989-y"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/DIAGNOSTICS12112718"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/J.AMC.2016.06.017"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/S22114079"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2901568"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2664042"},{"key":"e_1_2_10_8_2","first-page":"29","article-title":"Automatic hyperparameter optimization in keras for the MediaEval 2018 medico multimedia task","volume":"18","author":"Borgli R.","year":"2018","journal-title":"MediaEval"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2022.030432"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.22967\/HCIS.2022.12.025"},{"key":"e_1_2_10_11_2","unstructured":"AliS. 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