Eye Disease Detection and Classification using Convolutional Neural Networks (CNN)

Abstract

This study presents an advanced AI-based system for detection andclassification of eye diseases using retinal images.The proposed framework leverages Convolutional Neural Networks (CNNs) to analyze fundus images and accurately identify conditions such as cataract, glaucoma,normal.The system includes image preprocessing techniques, such as resizing, normalization, noise reduction, and contrast enhancement, to improve input quality for the CNN model. Once processed, the CNN extracts relevant features from the retinal images and classifies them into disease categories, providing confidence scores for each prediction.This system reduces reliance on manual examination, accelerates early diagnosis, and enhances the accuracy of eye disease detection, supporting ophthalmologists and researchers in clinical and educational applications. By integrating automated image analysis with intuitive visualization, the framework promotes efficient, reliable.

Introduction

The study focuses on developing an automated Eye Disease Detection and Classification System to improve early diagnosis of conditions like cataract, glaucoma, and other retinal diseases, which are major causes of vision impairment and blindness. Traditional diagnosis relies on manual examination of retinal images, which is time-consuming, labor-intensive, and prone to human error.

Leveraging high-resolution retinal imaging and deep learning (DL), especially Convolutional Neural Networks (CNNs), the system automatically extracts hierarchical features from retinal images—such as blood vessels, optic discs, and lesions—to classify diseases accurately. Image preprocessing steps like resizing, normalization, noise reduction, contrast enhancement, and data augmentation improve model reliability and generalization.

The methodology follows a structured multi-stage pipeline:

1. Image Acquisition – Retinal images sourced from clinical devices or datasets like EyePACS and Messidor.

2. Image Preprocessing – Standardization, noise reduction, contrast enhancement, and data augmentation for consistent input.

3. Feature Extraction & CNN Training – Convolutional and pooling layers detect anatomical and pathological patterns; fully connected layers classify diseases using softmax probability scores.

4. Disease Classification – Multi-class classification identifies conditions with confidence scores.

5. Result Visualization & Reporting – User-friendly interface displays annotated images, confidence metrics, and downloadable reports.

The system incorporates data security, role-based access, cloud storage, and optimization strategies such as hyperparameter tuning, model regularization, and resource management to enhance efficiency, scalability, and robustness.

Results indicate high accuracy in detecting multiple eye diseases, with visualizations improving interpretability for clinicians. Real-time predictions and downloadable reports make the system practical for clinical use, reducing reliance on manual examination, accelerating diagnosis, and supporting proactive patient care.

Overall, the study demonstrates the potential of AI-assisted ophthalmology to transform eye care through automated, reliable, and interpretable disease detection and classification.

Conclusion

The research successfully demonstrates an AI-based system for the detection and classification of eye diseases using Convolutional Neural Networks (CNNs). The system efficiently processes retinal images, automatically extracts meaningful features, and classifies them into categories such as glaucoma, diabetic retinopathy, cataract, and normal retinal conditions with high accuracy.Through image preprocessing, feature extraction, and deep learning-based classification, the system is able to detect subtle patterns and abnormalities in retinal images that may be missed during manual examination. The results show that the model provides reliable predictions, with annotated visualizations highlighting regions of interest, thereby supporting interpretability and aiding ophthalmologists in decision-making. Overall, the proposed method bridges the gap between manual examination and automated screening, enabling early detection of eye diseases, reducing human error, and improving patient outcomes. With its high accuracy, real-time prediction capability, and user-friendly interface for visualization and reporting, the system has the potential to significantly enhance clinical efficiency, support proactive healthcare, and advance AI-assisted ophthalmology.

References

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Copyright

Copyright © 2025 C. J. Jervin, U. Mohammed Abuthahir, M. S. Sangamithra. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.