• Pratik Hanmante
• Yash Gulhane
• Hariom Kumar
• Mrs. P.M. Sawant

Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes lesions on the retina that Effect vision. If it is not detected early, it can lead to blindness. Unfortunately, DR is not a reversible process, and Treatment only sustains vision. DR early detection and treatment can significantly reduce the risk of vision loss. The manual diagnosis process of DR retina fundus images by ophthalmologists is time-, effort-, and Cost-Consuming and prone to misdiagnosis unlike computer-aided diagnosis systems. In this paper, we present a deep learning-based approach for the detection and classification of diabetic retinopathy using retinal fundus images. The proposed system architecture uses a Convolutional Neural Network (CNN) to perform multi-class classification on the DR stage of the patient. We present a comprehensive evaluation of the proposed system on a large dataset of retinal fundus images and compare its performance with the state-of-the-art methods. The results demonstrate the robustness and efficacy of our proposed method for the detection of diabetic retinopathy

Diabetic retinopathy, Computer vision, Medical image analysis, Convolutional neural networks, Retinal fundus images

"Deep Learning Approach for Diabetic Retinopathy Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.313-316, May-2023, Available :http://www.jetir.org/papers/JETIRFX06055.pdf

"Deep Learning Approach for Diabetic Retinopathy Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. pp313-316, May-2023, Available at : http://www.jetir.org/papers/JETIRFX06055.pdf