Rhaman, KhalilurSayem, Tanvir IslamSara, Fouzia RahmanBiswas, PoromaBhowmick, Debabrata2025-02-232025-02-2320242024ID 20301360ID 20101122ID 20201084ID 20301374http://hdl.handle.net/10361/25532Cataloged from PDF version of thesis.Includes bibliographical references (pages 34-36).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024.From mild to severe distant vision impairment caused by untreated conditions, including cataract, glaucoma, retinal disease, and diabetic retinopathy, more than60% of the world’s population—exceeding 4.5 billion individuals—requires corrective lenses or treatments for visual and retinal disorders. The fundamental goal ofthe current study is to create an advanced deep learning (DL) system capable ofcategorizing retinal pictures into five groups. A deep convolutional neural network(CNN) was used to classify normal eyes, cataracts, glaucoma, retinal illness, and diabetic retinopathy. The dataset, obtained from Kaggle, had 2827 pictures that wererandomly divided into training, validation, and testing groups. The TensorFlowobject identification framework was used to create many CNN meta-architectures,including YOLOv5, YOLOv7, and InceptionResNet50. The YOLOv5 model showedgreat development. The YOLOv5 model demonstrated significant progress in detecting the mentioned eye diseases and achieving 0.951 mAP for 7357 images.52 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Eye DiseasesDeep learningYOLOv7PredictionInception-Resnet50YOLOv5Machine learningCognitive learning theoryThesis