Study on Deep Learning CNNs for Automated Eye Disease Detection Using Retina Scans

Abstract

Rapid advancements in the industry, especially machine learning (ML) and deep learning (DL), have made automated disease detection a prominent field. These methods have been frequently used to solve biomedical categorization issues, such as the diagnosis of eye diseases. Preventing irreversible vision loss requires early detection of eye conditions; however, manual diagnosis remains a complex and challenging process to scale. To aid in the diagnosis of ocular disease (OD), current research has focused on utilizing ensemble deep learning models based on Convolutional Neural Networks (CNNs). Several networks—EfficientNet, ResNet50, RegNetY040, MobileNetV2, InceptionV3, Xception, and DenseNet201—are used in this study to extract pertinent features and manage the multiclass classification task from a pre-processed dataset of fundus images. The best architecture is determined as a possible remedy for enhancing OD diagnosis after the models are assessed using accuracy and F1-score criteria.

Introduction

The text presents a comprehensive review of AI and machine learning approaches for medical image-based disease detection, especially focusing on ocular (eye) disease diagnosis, along with extensions to maternal health prediction and emerging quantum computing applications.

It explains that early and accurate diagnosis of eye diseases such as glaucoma, cataracts, and diabetic retinopathy is challenging due to limited medical resources and increasing case complexity. To address this, researchers have widely applied deep learning models—especially CNN architectures like EfficientNet, ResNet50, VGG16, DenseNet, InceptionV3, and MobileNetV2—on large fundus image datasets for multi-class and multi-label disease classification. Many studies report high accuracy and improved performance using transfer learning, ensemble methods, and optimized training techniques.

The literature shows a strong trend toward multi-label classification, since patients often suffer from multiple eye conditions simultaneously. Techniques such as data preprocessing, feature extraction (GLCM, LBP), and hybrid models have further improved results. However, challenges like dataset imbalance, model interpretability, privacy concerns, and real-world deployment remain significant issues.

The review also highlights the importance of Explainable AI (XAI) for making predictions transparent, and machine learning methods (like Random Forest and XGBoost) for related healthcare prediction tasks such as maternal risk assessment. In these applications, interpretability and accuracy are crucial for clinical trust and decision-making.

Additionally, the text explores emerging research in quantum machine learning, suggesting that quantum CNNs and quantum image processing techniques may enhance computational efficiency and scalability in future medical imaging systems.

References

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Copyright © 2026 Tushar Gupta, Sanidhya Singh, Sudheer Singh. 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.