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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 6

Published on: June 2026

DEEP LEARNING BASED: DIABETIC RETINOPATHY DETECTION USING RETINAL FUNDUS IMAGES

Y. N. Sakhare Vihang Dandawar Tushar Farande Rushikesh Ranaware Shreeharsh Shinde

Department of Information Technology

Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology Baramati, Pune, India
DOI:https://doi.org/10.55041/ijcope.v2i6.248

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

Diabetic Retinopathy (DR) remains a leading cause of vision impairment globally, necessitating robust automated screening tools to alleviate the burden on ophthalmologists. This paper presents an end-to-end production-grade pipeline for DR grading using a fine-tuned MobileNetV2 architecture. Leveraging a unified dataset of 1.15 lakh retinal fundus images from EyePACS, APTOS 2019, and Messidor-2 repositories, we address the critical challenges of data noise and class imbalance. Our methodology integrates a sophisticated OpenCV-based pre-processing engine utilizing Contrast Limited Adaptive Histogram Equalization (CLAHE) and smart contour cropping to amplify microscopic lesions. To ensure viability for edge deployment on constrained hardware (2GB VRAM), we implemented selective fine-tuning with Batch Normalization freezing and utilized Cate-gorical Focal Loss to prioritize high-severity cases. Experimental results demonstrate a peak validation accuracy of 75.83% and a high clinical specificity, achieving a 94% recall for healthy retinas. The system is deployed via a full-stack architecture featuring a FastAPI backend and a Streamlit dashboard, incorporating Test-Time Augmentation (TTA) to provide stable, clinical-grade diagnostics.

Index Terms—Deep Learning, Skin Disease Classification, Computer-Aided Diagnosis, EfficientNetV2, Vision Transformer (ViT), Image Segmentation, Medical Image Analysis

How to Cite this Paper

Sakhare, Y. N., Dandawar, V., Farande, T., Ranaware, R. & Shinde, S. (2026). Deep Learning based: Diabetic Retinopathy Detection using Retinal Fundus Images. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.248

Sakhare, Y., et al.. "Deep Learning based: Diabetic Retinopathy Detection using Retinal Fundus Images." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.248.

Sakhare, Y.,Vihang Dandawar,Tushar Farande,Rushikesh Ranaware, and Shreeharsh Shinde. "Deep Learning based: Diabetic Retinopathy Detection using Retinal Fundus Images." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.248.

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References


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  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
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  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Jun 18 2026
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