Screening diabetic retinopathy and exudates in retinal images using dual functional convolutional neural networks

Abstract

Exudates are the abnormal pattern lesions in Diabetic Retinopathy (DR) images, which is the primary cause of DR in diabetic patients. Therefore, its detection process is essential for further severity estimation of DR images. Automated detection systems’ robustness can be hampered by training datasets that are not representative. In this paper, the DR image is detected and classified using the proposed Dual Functional Convolutional Neural Networks (DFCNN). This proposed system consists of a noise detection and removal subblock, enhancement subblock, Gabor transform subblock, Feature computation subblock, and the proposed CNN classification architecture subblock. This proposed methodology is divided into two modules: a training module and a testing module. In the training module, the proposed DFCNN classification architecture is used to train both the abnormal case (DR case) and the healthy case (HCA) retinal images. In the testing module, the test source retinal image is classified as either a beneficial or DR case retinal image. From the classified DR case retinal image, the exudates are identified through the segmentation process in this research work. This developed DR and exudates detection system stated in this paper are evaluated on DRIVE, and DIARETDB1 retinal images and the experimental results are conducted with respect to various performance metrics in this paper. Once the suggested methodology was compared to other classification approaches, the proposed method’s accuracy was found to be greater (99.4 %).