Diabetic retinopathy detection with fundus images based on deep model enabled chronological rat swarm optimization.

Diabetic Retinopathy (DR) is termed as ever-lasting retinal disorders which can cause loss of vision and even blindness in most of the cases. The procedure to classify the level of severity in DR and is complex process as lesion features are complex to examine. The task of screening needs an effectual detection technique for classifying the subtle pathologies in retina. The deep models is considered as an imperative domain treating the disease caused in eye and help the ophthalmologists to make timely decisions. A new LeNet model is developed with Chronological Rat Swarm Optimization (CRSO) for detecting DR. The first step is the capturing retinal images through fundus photography, which further undergoes pre-processing with median filter. The segmentation is then achieved wherein lesion segmentation is completed with U-Net and blood vessel segmentation is performed with Dense U-Net. From input fundus image, lesion segmented output, and blood vessel segmented output, extraction of essential feature is performed. Finally, DR detection is executed with CRSO-based LeNet. The CRSO-based LeNet attained superior performance with highest accuracy of 89%, NPV of 87.2%, PPV of 87.0%, TNR of 89.0% and TPR of 89.5%. The proposed method is useful in various applications, such as medical applications and healthcare applications. [ABSTRACT FROM AUTHOR]