Automated Smartphone based System for Diagnosis of Diabetic Retinopathy
This provides a point-of-care diagnostic tool for diabetic people in remote areas, addressing shortages in healthcare access, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of early diagnosis of diabetic retinopathy in rural areas by developing an automated smartphone-based system using an inception-based CNN and binary decision tree ensemble, which was implemented in a smartphone app for offline and automatic diagnosis.
Early diagnosis of diabetic retinopathy for treatment of the disease has been failing to reach diabetic people living in rural areas. Shortage of trained ophthalmologists, limited availability of healthcare centers, and expensiveness of diagnostic equipment are among the reasons. Although many deep learning-based automatic diagnosis of diabetic retinopathy techniques have been implemented in the literature, these methods still fail to provide a point-of-care diagnosis. This raises the need for an independent diagnostic of diabetic retinopathy that can be used by a non-expert. Recently the usage of smartphones has been increasing across the world. Automated diagnoses of diabetic retinopathy can be deployed on smartphones in order to provide an instant diagnosis to diabetic people residing in remote areas. In this paper, inception based convolutional neural network and binary decision tree-based ensemble of classifiers have been proposed and implemented to detect and classify diabetic retinopathy. The proposed method was further imported into a smartphone application for mobile-based classification, which provides an offline and automatic system for diagnosis of diabetic retinopathy.