Human Eye Disease Detection Using CNN

Eye diseases such as glaucoma and cataract lead to severe vision loss. Early detection is essential but often delayed due to lack of specialists. This study presents a deep learning-based system using Convolutional Neural Networks (CNN) to automatically detect these conditions from retinal images. Our model achieved promising accuracy, providing a reliable, fast, and scalable diagnostic tool

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-06-06
DOI
https://doi.org/10.22214/ijraset.2025.70865
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

Human Eye Disease Detection Using CNN

Rishikesh P. Chavan
International Journal for Research in Applied Science and Engineering Technology
Brain Tumor Detection and Classification
article

Human Eye Disease Detection Using CNN

Rishikesh P. Chavan
article en

Abstract

Eye diseases such as glaucoma and cataract lead to severe vision loss. Early detection is essential but often delayed due to lack of specialists. This study presents a deep learning-based system using Convolutional Neural Networks (CNN) to automatically detect these conditions from retinal images. Our model achieved promising accuracy, providing a reliable, fast, and scalable diagnostic tool

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(6)
Openalex Percentile: Top 83%
Brain Tumor Detection and Classification
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Human Eye Disease Detection Using CNN — Rishikesh P. Chavan · International Journal for Research in Applied Science and Engineering Technology (2025) | TGRS Research Map | TGRS