<![CDATA[Diabetic retinopathy (DR) is a pervasive public health challenge, particularly in underserved rural regions like Nepal. Timely detection and management of DR are imperative to prevent vision impairment and blindness among individuals with diabetes. However, the scarcity of advanced medical facilities and trained ophthalmologists in remote areas hampers early diagnosis. This study addresses this critical issue by harnessing deep learning technology, specifically MobileNet, to develop an accessible and cost-effective solution for the early detection of diabetic retinopathy in Nepal's rural communities. The research aims to design and implement a robust DR detection system capable of operating on resource-constrained mobile devices. MobileNet, renowned for its efficiency and suitability for mobile applications, serves as the cornerstone of the solution. Through fine-tuning MobileNet on a comprehensive dataset comprising diverse DR manifestations, the model learns to accurately classify the presence of retinopathy. Recognizing the challenge of limited internet connectivity in rural Nepal, the project explores on-device inference techniques. This enables the model to run directly on mobile devices, minimizing the dependence on continuous internet access and making the diagnostic tool available even in remote regions. The anticipated impact of this project is twofold: firstly, it empowers healthcare workers in rural Nepal to provide early and precise DR screenings, facilitating timely intervention and reducing avoidable blindness. Secondly, it demonstrates the feasibility of deploying deep learning and mobile technology to address healthcare challenges in resource-limited settings beyond diabetic retinopathy.By harnessing MobileNet-based deep learning, this solution has the potential to significantly enhance healthcare accessibility and contribute to the overall well-being of the diabetic population in underserved areas.]]>
