IRO Journals
Journal of Soft Computing Paradigm

AI-based Retinal Image Classification for Early Detection of Eye Diseases

Open Access
Published: 26 May, 2026
Pages: 143-157
  • Ananthakumari A. , Ananthakumari A.
    Assistant Professor, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
    Assistant Professor, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
  • Muthu Menaka N. , Muthu Menaka N.
    Student, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
    Student, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
  • Kaniska M. Kaniska M.
    Student, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
    Student, Department of Computer Science and Engineering, Dr. G U Pope College of Engineering, Thoothukudi, India
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How to Cite

A., Ananthakumari, Muthu Menaka N., and Kaniska M. 2026. “AI-Based Retinal Image Classification for Early Detection of Eye Diseases”. Journal of Soft Computing Paradigm 8 (2): 143-57. https://doi.org/10.36548/jscp.2026.2.004.
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Keywords

Eye Disease Detection
Retinal Image Classification
Vision Transformer (ViT)
Deep Learning
Medical Image Analysis
Streamlit

Abstract

Early detection of eye diseases are essential to prevent severe vision impairment and improve patient care. Eye diseases such as cataract, glaucoma, and diabetic retinopathy are among the leading causes of vision loss if not diagnosed at an early stage. The goal of this project is to use artificial intelligence to develop an automated classification system for retinal images, which uses retinal fundus images to classify the four most common retinal diseases (i.e., cataract, diabetic retinopathy, glaucoma, and normal). We designed an image classification system which uses the Vision Transformer (ViT) deep learning model to classify images into four categories: cataract, diabetic retinopathy, glaucoma, and normal. Before training occurred, all images were pre-processed by using various methods of resizing, normalizing, and applying data augmentations to enhance the model's performance and generalization. The performance metrics used to evaluate the overall performance of the trained model were accuracy, precision, recall, F1-score, confusion matrix, and ROC curve. Overall, the proposed model achieved an average overall accuracy of 93.48% on the test dataset.

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