A New Strategy for the Detection of Diabetic Retinopathy using a Smartphone App and Machine Learning Methods Embedded on Cloud Computer

Abstract

Diabetes is a major cause of blindness, kidney failure, heart attacks, stroke and lower limb amputation according to World Health Organization (WHO). Complications from poor diabetes management lead to the Diabetic Retinopathy (DR) which is a leading cause of acquired blindness in the working-age population worldwide. WHO estimated that DR accounts for ≈ 15-17% of all cases of total blindness in the US and Europe, 7% of all cases in China and Mongolia. In Brazil, according to the Ministry of Health, the disease affects 7.6% of the population. A cost saving intervention includes screening and treatment for retinopathy. Detecting the different lesions related to DR plays an important role towards the stage detection, prediction, and prevention. Our challenge here is to design a deep learning neural network able to fully detect such lesions in digital retinal fundus image to help building robust and scaled solutions to tackle this urgent diabetes scenario. These results show that the Diavision Portable device had a better performance using GLCM, LBP and SVM, presenting 100% for both evaluation metrics considered. Using Messidor dataset were obtained better performance with VGG16 and SVM, archiving 100% for all metrics. In terms of feature extraction time, the GLCM and VGG16 presented acceptable times, respectively, 17.63ms and 37.87ms.