Point-of-Care Diabetic Retinopathy Diagnosis: A Standalone Mobile Application Approach

Although deep learning research and applications have grown rapidly over the past decade, it has shown limitation in healthcare applications and its reachability to people in remote areas. One of the challenges of incorporating deep learning in medical data classification or prediction is the shortage of annotated training data in the healthcare industry. Medical data sharing privacy issues and limited patient population size can be stated as some of the reasons for training data insufficiency in healthcare. Methods to exploit deep learning applications in healthcare have been proposed and implemented in this dissertation. Traditional diagnosis of diabetic retinopathy requires trained ophthalmologists and expensive imaging equipment to reach healthcare centres in order to provide facilities for treatment of preventable blindness. Diabetic people residing in remote areas with shortage of healthcare services and ophthalmologists usually fail to get periodical diagnosis of diabetic retinopathy thereby facing the probability of vision loss or impairment. Deep learning and mobile application development have been integrated in this dissertation to provide an easy to use point-of-care smartphone based diagnosis of diabetic retinopathy. In order to solve the challenge of shortage of healthcare centres and trained ophthalmologists, the standalone diagnostic service was built so as to be operated by a non-expert without an internet connection. This approach could be transferred to other areas of medical image classification.