  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.
