Meta Heuristic Fusion Model for Classification with Modified U-Net-based Segmentation

Abstract

General cause of diabetes mellitus is Diabetic Retinopathy (DR), which outcomes in lesions on the retinas that impair vision. If it is not detected in time, the result is severe blindness issues. Regrettably, there is no treatment for DR. Early diagnosis and treatment of DR can greatly lower the risk of visual loss. In contrast to computer-aided diagnosis methods, the manual diagnosis of DR using retina fundus images is more time-consuming effort, and high cost as well, as it is highly prone to error. Deep learning has emerged as one of the most popular methods for improving performance, particularly in the classification and analysis of medical images. Therefore, a deep structure-based DR detection and severity classification has been demonstrated for treating the DR with the usage of fundus images. The major aim of this developed technique is to classify the severity level of the retinal region of the human eye from the fundus images. At first, the required retinal fundus images are collected from the standard benchmark data sources. Secondly, image enhancement techniques are applied to the collected fundus images to improve the quality of images. Thirdly, the abnormality segmentations are carried out by using the optic disc removal process using active contouring model and then, the regional segmentation is done via the Modified U-Net method. Finally, the segmented image is subjected to the hybrid classifier network named a Hybrid Soft Attention-based DenseNet with Multi-Scale Gated ResNet (HSADMGR Net) for classifying the retinal fundus images and finding the severity level of the retinal images with higher accuracy. Furthermore, the parameters present inside the hybrid classifier network are optimized with the help of implemented Multi-Armed Bandits Groundwater Flow Algorithm (MABGFA). The test results regarding the developed deep structure-based DR model are validated with the existing DR detection and classification approaches by using different performance measures.