Gunjan Kande ,Tanmay Khandiat, Lishika Seerwani, Santosh Randive

Diabetic retinopathy (DR) is a consequential complication arising from diabetes, impacting the retina and potentially leading to vision impairment or even blindness. This project centers on the development of an advanced diagnostic system for the early identification and assessment of diabetic retinopathy severity through the analysis of fundus images. The project’s framework involves the application of deep learning methodologies, a subset of artificial intelligence, to analyze and interpret biomedical images. Specifically, Convolutional Neural Networks (CNNs) are utilized to discern intricate patterns and features within retinal images, enabling precise identification of retinopathic abnormalities. To ensure the model’s robustness across diverse clinical scenarios, the project harnesses curated datasets comprising a varied range of fundus images for training. The primary objective is to create a software tool capable of automatically discerning different levels of diabetic retinopathy severity upon uploading fundus images. The project’s outcomes underscore the effectiveness of the developed system in providing swift and reliable assessments, enabling timely intervention and management of diabetic retinopathy. In conclusion, the incorporation of deep learning into biomedical imaging emerges as a promising avenue for improving the early detection of diabetic retinopathy. This research contributes to ongoing endeavors aimed at enhancing healthcare outcomes for individuals at risk of this sight_x0002_threatening complication.

https://doi.org/10.58357/jedcs.2024.v3.i2.13