Ar-HGSO: Autoregressive-Henry Gas Sailfish Optimization enabled deep learning model for diabetic retinopathy detection and severity level classification

Abstract

Diabetic Retinopathy (DR) is one the most important problems of diabetics and it directs to the main cause of blindness. When proper treatment is afforded for DR patients, almost 90% of patients are protected from visual damage. DR does not produce any symptoms at the initial phase of the disease, thus various physical assessments, namely pupil dilation, visual acuity test, and so on are needed for DR disease detection. It is more complex to detect the DR during manual testing, because of the variations and complications of DR. The early detection and appropriate treatment assist to prevent vision loss for DR patients. Thus, it is very indispensable to categorize the levels and severity of DR for recommendation of essential treatment. In this paper, Autoregressive-Henry Gas Sailfish Optimization (Ar-HGSO)-based deep learning technique is proposed for DR detection and severity level classification of DR and Macular Edema (ME) based on color fundus images. The segmentation process is more essential for proper detection and classification process, which segments the image into various subgroups. The Deep Learning approach is utilized for effective identification of DR and severity classification of DR and ME. Moreover, the deep learning technique is trained by the designed Ar-HGSO scheme for obtaining better performance. The performance of the devised technique is evaluated using the IDRID dataset and DDR dataset. The introduced Ar-HGSO-based deep learning approach obtained better performance than other existing DR detection and classification techniques with regards to testing accuracy, sensitivity, and specificity of 0.9142, 0.9254, and 0.9142 using the IDRID dataset.