Rahman, RafeedDofadar, Dibyo FabianRahaman, AsifMahamud, ShifatAkter, ShanjidaSaha, DiproFahad2024-06-242024-06-24©20232023-09ID 19101605ID 19101621ID 20101627ID 19101614ID 19101486http://hdl.handle.net/10361/23550Cataloged from PDF version of thesis.Includes bibliographical references (pages 34-38).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.The number of people living with blindness is about 43 million people and 295 million people are living with moderate-to-severe visual impairment. The leading causes of most blindness are macular degeneration, diabetic retinopathy, and glaucoma. Moreover, the early stages of most eye diseases are asymptomatic. As a result, determining the cause becomes very difficult, and if left untreated, there can be irreversible damage to vision. This paper discusses a hybrid structure that combined ResNet50 and VGG19 to successfully classify and predict various eye diseases accurately. In addition, we used transfer learning and multi-class classification, which gave us an accuracy of 94.7%, whereas previous approaches with traditional CNN only gave an accuracy of less than 85%. This study has the potential to significantly contribute to the timely identification and precise categorization of ocular disorders, hence leading to advancements in patient treatment, increased overall well-being, and a more promising outlook for individuals affected by visual disabilities. Moreover, it indicates the possibility of wider utilization of sophisticated deep learning methods in the field of medical image analysis.enBrac 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.Hybrid structureResnet50VGG19Multi-class classificationEye--DiseasesData miningThesis