Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images

In recent days, progressive rate of diabetic retinopathy (DR) becomes high and it is needed to develop an automated model for effective diagnosis of DR. This paper presents a new deep neural network with moth search optimization (DNN-MSO) algorithm based detection and classification model for DR images. The presented DNN-MSO algorithm involves different processes namely preprocessing, segmentation, feature extraction and classification. Initially, the contrast level of the DR images is improved using contrast limited adaptive histogram equalization model. After that, the preprocessed images are segmented using histogram approach. Then, Inception-ResNet V2 model is applied for feature extraction. Finally, extracted feature vectors are given to the DNN-MSO based classifier model to classify the different stages of DR. An extensive series of experiments were carried out and the results are validated on Messidor DR dataset. The obtained experimental outcome stated the superior characteristics of the DNN-MSO model by attaining a maximum accuracy, sensitivity and specificity of 99.12%, 97.91% and 99.47% respectively.