Middle East Research Journal of Engineering and Technology | Volume 4 | Issue-4 | Pages: 124-129 Diabetic Retinopathy Detection

Abstract: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which affects the retina and can lead to impaired vision or blindness if detected and treated late. The project develops an A.I. based diagnostic system whose SDL with classified grading for DR identification using retinal fundus images. The system uses CNNs-based medical image analysis and divides it into five different classes: No_DR, Mild, Moderate, Severe, and Proliferative_DR. Image preprocessing enhances the extraction of features and prediction accuracy. To further improve accessibility, the project includes a web application based on Flask which allows users to upload their images and instantly receive a diagnostic report. The development also reduces medical professionals' workload while providing a scalable way to massively address the rising tide of diabetes-related blindness, thus making quality eye care available everywhere.

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Publication Details

Journal
Middle East Research Journal of Engineering and Technology
Published
2024-12-28
DOI
https://doi.org/10.36348/merjet.2024.v04i04.005
Primary Topic
Retinal Imaging and Analysis
Type
article
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article

Middle East Research Journal of Engineering and Technology | Volume 4 | Issue-4 | Pages: 124-129 Diabetic Retinopathy Detection

M Jahnavi, M. Yusuf K, Vijay Hegde, Mulkh Raj et al.
Middle East Research Journal of Engineering and Technology
Retinal Imaging and Analysis
article

Middle East Research Journal of Engineering and Technology | Volume 4 | Issue-4 | Pages: 124-129 Diabetic Retinopathy Detection

M Jahnavi, M. Yusuf K, Vijay Hegde, Mulkh Raj, M Waseeha, R Yashika
article en

Abstract

Abstract: Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which affects the retina and can lead to impaired vision or blindness if detected and treated late. The project develops an A.I. based diagnostic system whose SDL with classified grading for DR identification using retinal fundus images. The system uses CNNs-based medical image analysis and divides it into five different classes: No_DR, Mild, Moderate, Severe, and Proliferative_DR. Image preprocessing enhances the extraction of features and prediction accuracy. To further improve accessibility, the project includes a web application based on Flask which allows users to upload their images and instantly receive a diagnostic report. The development also reduces medical professionals' workload while providing a scalable way to massively address the rising tide of diabetes-related blindness, thus making quality eye care available everywhere.

Middle East Research Journal of Engineering and TechnologyVol. 4(04)
Openalex Percentile: Top 55%
Retinal Imaging and Analysis
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Middle East Research Journal of Engineering and Technology | Volume 4 | Issue-4 | Pages: 124-129 Diabetic Retinopathy Detection — M Jahnavi, M. Yusuf K, et al. · Middle East Research Journal of Engineering and Technology (2024) | TGRS Research Map | TGRS