Implementation Of Diagnostic Model for Eye Disease Detection Using Artificial Intelligence Using Optimized Threshold and Restnet 50 |
Author(s): |
| Dhananjay Phattesing Mane , DY Patil College of Engineering, Pune; Dr. Kalyan Devappa Bamane, DY Patil College of Engineering, Pune; Srushti Laxman Patil, DY Patil College of Engineering, Pune; Shruti Manohar Dikkar, DY Patil College of Engineering, Pune; Anshu Anil Dudhagundi, DY Patil College of Engineering, Pune |
Keywords: |
| Vision-Threatening Diseases, Artificial Intelligence (AI), Machine Learning (ML), Retinal Image Analysis, Early Diagnosis, Affordable Healthcare, Scalable Diagnostics, Continual Learning, Macular Degeneration, Glaucoma, Cataracts, Diabetic Retinopathy, Accessibility, Low-Resource Areas |
Abstract |
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Macular degeneration, glaucoma, cataracts, and diabetic retinopathy are among the vision-threatening conditions that represent a serious global health concern. Costly and specialized techniques can impede early and correct diagnosis, which is essential for successful therapy, particularly in underprivileged regions. We have created an AI-powered online application that analyzes retinal images using the ResNet50 model in order to close this gap. This platform offers scalable and reasonably priced diagnostic assistance for the early detection of eye diseases, providing thorough reports that empower patients and medical professionals. Its ability to learn continuously guarantees that accuracy will increase over time, providing a potent weapon to fight avoidable blindness and vision impairment globally. |
Other Details |
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Paper ID: IJSRDV13I40077 Published in: Volume : 13, Issue : 4 Publication Date: 01/07/2025 Page(s): 129-139 |
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