Diabetic retinopathy is one of the complications of diabetes leading to blindness. Early detection through periodic screening of retinal images can help reduce the long-term effects and blindness by as much as 90%. Its resource demands, however are high requiring eye doctors and equipment. This is particularly problematic in remote and resource-poor areas where the detection is most needed. With half billion people already living with diabetes worldwide, and their number growing at a rapid pace due to rapid industrialization, unhealthy diets and sedentary lifestyles, innovative technology-enabled solutions are essential.
Imagine if artificial intelligence (AI) technologies, in particular deep learning, could offer a way to implement real-time screening at relatively low cost and take retinal screening to the diabetic patient in primary care.You are invited to assist Assoc. Prof. Ravi Seethamraju from the University of Sydney Business School and Prof. Krishna Sundar of the Digital Innovation Lab at the Indian Institute of Management Bangalore, in designing a low-cost solution.
The challenge is to develop a model that could classify diabetic retinopathy images into five categories [Proliferative DR (PDR), Normal Non-proliferative DR (NPDR), mild NPDR, moderate NPDR and severe NPDR with ratings of 0, 1, 2, 3 and 4 respectively] and achieve 85%+ validation and testing accuracy, for a given dataset of images. You will be free to use any publicly available dataset of your choice for building, training and validating your model.
Prize pool: $10,000
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