Microaneurysms detection using fundus images based on deep convolutional neural network enabled fractional hunter osprey optimization

Diabetic Retinopathy (DR) is one of the foremost reasons for poor eyesight in the modern globe. An earlier detection of DR is vital in offering effectual treatment. Moreover, detecting the severity levels of DR, such as Microaneurysms (MA), Hemorrhages (HMs), Exudates (EXs) and extra development of blood vessel recognition using fundus images are challenging chores owing to the complex structures and shapes of lesions in the fundus image. This paper aims to develop a technique for DR detection at an early stage. MA is the first symptom of DR that leads to blood leakage in the retina. Here, Fractional Hunter Osprey Optimization-Deep Convolutional Neural Network (FHOO-DCNN) is introduced for MA detection. An input image pre-processing is executed by an adaptive wiener filter and then, optic disc (OD) detection is accomplished. The Bayesian U-Net is used for OD detection, which is tuned by Hunter Osprey Optimization (HOO). The HOO is modelled by combining Hunter–Prey Optimizer (HPO) with the Osprey Optimization Algorithm (OOA). Blood vessel segmentation is conducted utilizing morphological Top-Hat transform. Thereafter, features from the input image, blood vessel segmented image, and OD-detected image are extracted. At last, MA detection is performed by DCNN that is tuned using FHOO. Furthermore, FHOO is joining of Fractional Calculus (FC) concept with HOO. In addition, FHOO-DCNN has acquired high accuracy, sensitivity and specificity of 91.1%, 91.2% and 90.4%. The proposed method is applicable in primary screening and regular clinical work for the monitoring of the progression of DR.