Retinal Image-Based Cardiovascular Risk Prediction Using Fractional Jellyfish Search Optimization-Enhanced Deep Learning
TL;DR
A deep learning (DL)-based framework is developed for CVD prediction using an FJSO-enhanced deep Q-Net (FJSO-Deep Q-Net) and experimental evaluation shows that the proposed method achieves superior classification performance.
Abstract
Cardiovascular disease (CVD) impairs the functioning of the heart and blood vessels, often leading to severe physical disability. Early and automated detection of CVD can therefore play an important role in saving lives. Although many studies have addressed this issue, there remains scope for improving accuracy and reliability. In this research, a deep learning (DL)-based framework is developed for CVD prediction. Initially, the retinal images are preprocessed, and the optic disc is identified using a pyramid scene passing network (PSP-Net) optimized by the fractional jellyfish search optimization (FJSO). Then, blood vessel segmentation is performed using an FJSO-optimized spatial attention U-Net (SA-UNet). After segmentation, the statistical features are extracted, and textural features are derived from the input data. Both sets of features contribute to the CVD detection process. Furthermore, CVD risk prediction is carried out using an FJSO-enhanced deep Q-Net (FJSO-Deep Q-Net). Experimental evaluation shows that the proposed method achieves superior classification performance, with accuracy, sensitivity, and specificity values of 0.934, 0.924, and 0.939, respectively.