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However, ophthalmologists are currently carrying out retinal eye disease detection manually with the help of OCT images that may be erroneous and subjective. Different methods have been presented to automate the manual retinal eye disease detection process that needs further improvement in detection accuracy. This research proposed an automatic method for retinal eye disease detection and classification from OCT images using fusion and selection techniques. First, the modified\u2010Alexnet and ResNet\u201050 are utilized for deep feature vector extraction. In the next step, these vectors are fused serially and rectified by the proposed feature selection framework and passed as input to different machine learning classifiers for retinal disease diagnosis. For this purpose, a publicly available dataset of retinal eye diseases with four classes is utilized. The proposed retinal eye disease detection method achieved an overall average accuracy index of greater than 99.95%, higher than the top one in the literature, that is, 99.39%. Experimental results authenticated that the proposed retinal eye disease detection methodology can reliably be used for automatic eye disease detection from OCT images. Furthermore, the proposed deep feature and selection\u2010based retinal eye disease detection methodology achieved state\u2010of\u2010the\u2010art performance.<\/jats:p>","DOI":"10.1111\/exsy.13232","type":"journal-article","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T04:22:47Z","timestamp":1674015767000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A deep feature fusion and selection\u2010based retinal eye disease detection from <scp>OCT<\/scp> images"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0868-5617","authenticated-orcid":false,"given":"Muhammad Junaid","family":"Umer","sequence":"first","affiliation":[{"name":"Department of Computer Science COMSATS University Islamabad, Wah Campus  Rawalpindi Pakistan"}]},{"given":"Muhammad","family":"Sharif","sequence":"additional","affiliation":[{"name":"Department of Computer Science COMSATS University Islamabad, Wah Campus  Rawalpindi Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9124-9298","authenticated-orcid":false,"given":"Mudassar","family":"Raza","sequence":"additional","affiliation":[{"name":"Department of Computer Science COMSATS University Islamabad, Wah Campus  Rawalpindi Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1939-4842","authenticated-orcid":false,"given":"Seifedine","family":"Kadry","sequence":"additional","affiliation":[{"name":"Department of Applied Data Science Noroff University College  Kristiansand Norway"},{"name":"Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology Ajman University  Ajman United Arab Emirates"},{"name":"Department of Electrical and Computer Engineering Lebanese American University  Byblos Lebanon"}]}],"member":"311","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.06.004"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-017-0712-9"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2017.11.014"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23172"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1108\/JEIM-02-2020-0076"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-019-02066-y"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23063"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/6838976"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2017.01.002"},{"key":"e_1_2_9_11_1","doi-asserted-by":"crossref","unstructured":"Anantrasirichai N. 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