Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Deep Learning for Early Diagnosis of Diabetic Retinopathy, Glaucoma, and Cataract from Fundus Images

Author(s) Puneet Misra, Mohd Usman, Uroosha Usman
Country India
Abstract Retinal disorders represent a growing global health crisis, particularly among the working-age population. While manual screening of fundus imagery is the standard, it remains a labour-intensive process dependent on specialized expertise. This study addresses the urgent demand for rapid, automated diagnostic tools by evaluating the efficacy of lightweight Deep learning (DL) architectures for multi-class retinal disease identification.
Utilizing a public dataset of 4,217 images, the study performed a comparative analysis of four Convolutional Neural Network (CNN) architectures: MobileNetV2, VGG16, DenseNet121, and EfficientNetB0. Each model was assigned with classifying images into four categories: Normal, Diabetic Retinopathy, Glaucoma, and Cataract. Experimental results identify MobileNetV2 as the superior model, achieving a peak accuracy of 94.08%, an F1-score of 0.9430, and a mean Average Precision (mAP) of 0.9786.
With a calculated efficiency score of 0.4157, MobileNetV2 demonstrates the ideal balance between high diagnostic precision and low computational demand. These findings highlight that streamlined CNNs are uniquely suited for real-time deployment in resource-constrained environments. Ultimately, this research provides a robust foundation for AI-assisted ophthalmic screening, facilitating earlier clinical intervention and improved patient outcomes globally.
Published In Volume 6, Issue 6, November-December 2025
Published On 2025-11-06
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E-ISSN 3048-7641
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