Aquila Optimizer based Deep Neural Network for Automatic Diabetic Retinopathy detection and classification

Abstract

Diabetic Retinopathy is a retinal disease that significantly affects working and aged people all over the world. Nowadays, numerous techniques are available for the early detection and diagnosing process which acts as a prevention strategy for visual impairment. Recent researches have provided a cheap way of identification but the execution time required for classifying an optimal output was very high. Therefore, to overcome such shortcomings, this paper proposes four different phases namely the preprocessing, segmentation stage, feature extraction stage as well as classification stage. During preprocessing phase, the images are cropped to eliminate the additional areas and unwanted noise signals are eliminated. Then a modified level set algorithm is employed in segmenting the retinal images from the preprocessed image. Finally, this paper utilizes a deep neural network-based Aquila optimizer (DNN based AO) to classify the retinal images. In addition, this paper utilizes a publically accessible IDRiD dataset containing 516 image sets to evaluate the effectiveness of the proposed approach. At last, the experimental evaluations are carried out and the analysis revealed that the performance rate of the proposed approach is high when compared with other existing techniques.