Abstract:
Vision impairment is one of the major problems affecting middle-aged individuals due to
uncontrolled blood sugar levels, commonly known as Diabetic Retinopathy (DR). The small
abnormalities in the retinal capillaries, called microaneurysms and intra retinal bleeding, are the
initial symptoms of Diabetic Retinopathy. Clinical identification of Diabetic Retinopathy is a time consuming and difficult process due to limitations in resources and experienced doctors. Early
detection is crucial in avoiding the progression of Diabetic Retinopathy, highlighting the importance
of an automated DR detection method to identify symptoms in its early stages. In this paper,
researchers developed an Enhanced Minimal Convolutional Neural Network (EMCNN) model to
classify Mild-DR and No-DR fundus images using a binary classification process. The fundus images
were pre-processed using Contrast Limited Adaptive Histogram Equalization (CLAHE) method
before passed through the network. EMCNN is an experimental model that enjoys a minimum
number of layers and batch normalization to minimize the training effort. Finally, the EMCNN model
is compared to existing models in terms of accuracy and efficiency