Eye Disease Classification Using Convolutional Neural Networks

Eye Disease Classification Using Convolutional Neural Networks

CH. Sathvika1 , D. Sai Sumanth2 , D.Vigneshwar Reddy3

Department of ECE, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India.

csatvika_ece2104d8@mgit.ac.in , , dsai_ece2104d9@mgit.ac.in dvigneshwar_ece2104e0@mgit.ac.in

Abstract - The eye is an essential sense organ responsible for vision. Since certain eye disorders can lead to vision loss, it is crucial to diagnose and treat these conditions at an early stage. By identifying common eye diseases and performing regular eye examinations, eye care professionals can help prevent vision impairment or blindness. In this work, Convolutional Neural Networks (CNNs) and transfer learning are used to differentiate between normal eyes and those affected by diabetic retinopathy, cataract, or glaucoma. By applying transfer learning for multi-class classification, our goal is to achieve high accuracy by leveraging pre-trained models on this specific dataset to obtain better results.