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Consequently, early detection of DME is essential for appropriate treatment to prevent significant vision loss. Moreover, early and effective treatment is key to controlling the disease, keeping vision intact, and achieving better overall outcomes. Accordingly, in this paper, an innovative model named Fractional Group Search Optimizer\u2010enabled FractalNet (FGSO_FractalNet) is introduced for DME detection. The detection process for diabetic macular edema begins by acquiring optical coherence tomography (OCT) images from the database. Next, layer segmentation is conducted on these OCT images using the E\u2010Net algorithm. Following segmentation, image augmentation is performed to enhance the dataset. Then, the features are extracted from the augmented OCT images. Finally, DME is detected using the proposed FractalNet, which is trained using fractional group search optimizer (FGSO). The FGSO is developed by integrating fractional calculus (FC) and group search optimizer (GSO). The proposed FGSO_FractalNet method achieved notable results with an accuracy of 91.333%, a sensitivity of 90.174%, and a specificity of 90.560% for dataset 1, showcasing its strong performance. Similarly, the proposed method attained 89.506% accuracy, 88.371% sensitivity, and 88.750% specificity for dataset 2.<\/jats:p>","DOI":"10.1002\/jemt.70001","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T04:50:57Z","timestamp":1750999857000},"page":"2809-2829","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FGSO_FractalNet: Fractional Group Search Optimizer\u2010Enabled <scp>FractalNet<\/scp> for Diabetic Macular Edema Detection Using <scp>OCT<\/scp> Image"],"prefix":"10.1002","volume":"88","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9295-1067","authenticated-orcid":false,"given":"Kiran","family":"Kadakuntla","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering SDM College of Engineering and Technology, Dharwad, Karnataka, India. Affiliated to Visvesvaraya Technological University, Belagavi\u2010590018  India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. V.","family":"Viraktamath","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering SDM College of Engineering and Technology, Dharwad, Karnataka, India. Affiliated to Visvesvaraya Technological University, Belagavi\u2010590018  India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05107-y"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iswa.2024.200334"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jestch.2021.101091"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22134675"},{"issue":"1","key":"e_1_2_9_6_1","first-page":"1","article-title":"Hybrid Deep Learning Models for the Screening of Diabetic Macular Edema in Optical Coherence Tomography Volumes","volume":"14","author":"Arruabarrena C.","year":"2024","journal-title":"Scientific Reports"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1150295"},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","unstructured":"Bangar M. andP.Chaudhary.2022.\u201cConvolutional Neural Networks for Automated Detection of Diabetic Macular Edema\u201d.","DOI":"10.21203\/rs.3.rs-1989082\/v1"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.62.13.20"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11152294"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/app14167262"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICIEV.2014.6850777"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12020532"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1097\/JCMA.0000000000000355"},{"issue":"4","key":"e_1_2_9_15_1","first-page":"290","article-title":"A Hybrid CNN\u2010SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images","volume":"43","author":"Khairandish M. O.","year":"2022","journal-title":"Innovation and Research in BioMedical Engineering"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-021-10066-4"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.4274\/tjo.galenos.2023.92635"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/I2CT51068.2021.9418050"},{"key":"e_1_2_9_19_1","unstructured":"Larsson G. 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