Diabetic retinopathy grading using deep neural networks

Abstract

Diabetic Retinopathy (DR) is a chronic disease and the leading cause of blindness and visual impairment among diabetic patients making early detection and classificatio nof DR crucial for effectiv etreatment .Thi sprojec tutilize sstate-of-the-ar tconvolutiona lneu- ral networks and transfer learning techniques to analyze retinal images and classify the severity of the disease on a scale of 0 to 4 (ranging from healthy to proliferative). We employ architectures such as EfficientNetB 1,InceptionV 3,Xceptio n,a ndMobileNet V2to achieve accurate classification .Throug h aserie so fexperiment san devaluations ,ou rsys- tem achieves an overall accuracy of 80.0% in classifying DR images. The results showcase the significan tpotentia lo fdee plearnin gi nassistin ghealthcar eprofessional swit hearly diagnosis and treatment planning, thereby improving patient well-being.