{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:20:41Z","timestamp":1760660441822,"version":"build-2065373602"},"reference-count":33,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T00:00:00Z","timestamp":1740614400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah Bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2025R346"],"award-info":[{"award-number":["PNURSP2025R346"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Microscopy Res &amp;amp; Technique"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>The retina is an important part of the eye that aids in focusing light and visual recognition to the brain. Hence, its damage causes vision loss in the human eye. Central serous retinopathy is a common retinal disorder in which serous detachment occurs at the posterior pole of the retina. Therefore, detection of CSR at an early stage with good accuracy can decrease the rate of vision loss and recover the vision to normal conditions. In the past, numerous manual techniques have been devised for CSR detection; nevertheless, they have demonstrated imprecision and unreliability. Thus, the deep learning method can play an important role in automatically detecting CSR. This research presents a convolutional neural network\u2010based framework combined with segmentation and post\u2010ocessing for CSR classification. There are several challenges in the segmentation of retinal images, such as noise, size variation, location, and shape of the fluid in the retina. To address these limitations, Otsu's thresholding has been employed as a technique for segmenting optical coherence tomography (OCT) images. Pigments and fluids are present in epithelial detachment, and contrast adjustment and noise removal are required. After segmentation, post\u2010processing is used, combining flood filling, dilation, and area thresholding. The segmented processed OCT scans were classified using the fusion of three networks: (i) ResNet\u201018, (ii) Google\u2010Net, and (iii) VGG\u201019. After experimentation, the fusion of ResNet\u201018, GoogleNet, and VGG\u201019 achieved 99.6% accuracy, 99.46% sensitivity, 100% specificity, and 99.73% <jats:italic>F<\/jats:italic>1 score using the proposed framework for classifying normal and CSR\u2010affected images. A publicly available dataset OCTID comprises 207 normal and 102 CSR\u2010affected images was utilized for testing and training of the proposed method. The experimental findings conclusively demonstrate the inherent suitability and efficacy of the framework put forth. Through rigorous testing and analysis, the results unequivocally validate the framework's ability to fulfill its intended objectives and address the challenges at hand.<\/jats:p>","DOI":"10.1002\/jemt.24836","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T12:10:39Z","timestamp":1741695039000},"page":"1947-1963","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Ensemble for Central Serous Microscopic Retinopathy Detection in Retinal Optical Coherence Tomographic Images"],"prefix":"10.1002","volume":"88","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0886-4140","authenticated-orcid":false,"given":"Syed Ale","family":"Hassan","sequence":"first","affiliation":[{"name":"Riphah Artificial Intelligence Research (RAIR) Lab, Riphah International University, Faisalabad Campus  Faisalabad Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6825-3851","authenticated-orcid":false,"given":"Shahzad","family":"Akbar","sequence":"additional","affiliation":[{"name":"Riphah Artificial Intelligence Research (RAIR) Lab, Riphah International University, Faisalabad Campus  Faisalabad Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ijaz Ali","family":"Shoukat","sequence":"additional","affiliation":[{"name":"Riphah Artificial Intelligence Research (RAIR) Lab, Riphah International University, Faisalabad Campus  Faisalabad Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0101-0329","authenticated-orcid":false,"given":"Amjad R.","family":"Khan","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University Riyadh  Riyadh Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0312-8731","authenticated-orcid":false,"given":"Faten S.","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences, College of Science Princess Nourah Bint Abdulrahman University  Riyadh Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3138-3801","authenticated-orcid":false,"given":"Tanzila","family":"Saba","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Data Analytics Lab, CCIS Prince Sultan University Riyadh  Riyadh Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,27]]},"reference":[{"key":"e_1_2_11_2_1","first-page":"1","article-title":"Rice Leaf Blast Disease Detection Using Multi\u2010Level Colour Image Thresholding","volume":"10","author":"Bakar M. 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