Real Time Detection of Diabetic Retinopathy using Deep Learning Techniques

Irfan Ali Bhacho; Hina Lilaram; Sarmad Talpur; Madeha Memon; Nouman Qadeer Soomro

Authors

Irfan Ali Bhacho Department of Computer Systems Engineering (Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan).
Hina Lilaram Department of Computer Systems Engineering (Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan).
Sarmad Talpur Department of Computer Systems Engineering (Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan).
Madeha Memon Department of Computer Systems Engineering (Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan).
Nouman Qadeer Soomro Department of Software Engineering (Mehran University of Engineering and Technology, SZAB Campus, Sindh, Pakistan).

Keywords:

Diabetic Retinopathy, Diabetes Mellitus, VGG16, ResNet50, Custom CNN Model

Abstract

Diabetic Retinopathy is a prevalence disease which is a medical condition frequently caused due to high sugar levels of blood. It deteriorates the optic nerve as it compresses and blurs the vision, which is used to detect white light and transmits signals to your cerebrum using a nerve. There has been a massive increase in the statistics having diabetic retinopathy which causes the loss of sight in any age group with no treatment Every diabetic patient is required to visit their ophthalmologist after every two weeks or mandatorily in a month. Moreover, bi-annual inspection is required to notice the amount of vision to see the objects. For this reason, Pakistan lacks to have ophthalmologist which are expert in their domain. Mostly, they are not available round the clock especially in less privileged areas. Therefore, we have developed a smartphone-based handheld AI-integrated product which is cost-effective and portable which detects the visual Impairment and produces reports of the concern patient with a minor intervention on the same day by an eye specialist. This research project focuses on diabetic retinopathy detection by utilizing 20D (20 Diopter) Lens and camera of any random smart phone which captures fundus images which are further spitted and compared against various models of deep learning . In this research, VGG-15, ResNet50 and Custom CNN was undertaken. As a result, VGG16 outperformed other models by obtaining highest validation accuracy that is 74.53% as well as lowest validation loss of 55.94%. Moreover, ResNet50 yielded 74.08% validation accuracy and computing validation loss of 58.72%. Consequently, Custom CNN Model achieves 57.26% validation accuracy and 57.26% validation loss. Thus, VGG16 performed best on the dataset provided and is deployed in the smartphone application which is a portable and cost-effective method for Diabetic Retinopathy screening in less privileged areas. This project aims to target three Sustainable Development Goals including Affordable and clean energy ,Good health and well-being, and Industry Innovation and Infrastructure respectively.

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