Intelligent decision support system for cardiovascular risk prediction using hybrid loss deep joint segmentation and optimized deep learning

Abstract

Cardiovascular disease (CVD) represents an emerging death reason worldwide. CVD is based on the capability to discover the high-risk individuals before designing overt events. An effective technique for CVD risk prediction is developed using retinal fundus images. Initially, the retinal fundus images are subjected to pre-processing using grayscale conversion. The optic disc is detected with binarization and circle fixing. Then, the blood vessel segmentation uses deep joint segmentation, wherein dice coefficient and binary cross-entropy are integrated. After that, the feature extraction is done for mining convenient features that include several statistical features. Meanwhile, features like Local Directional Texture Pattern (LDTP) and Local Gabor Binary Pattern (LGBP) are mined from the inputted image. Then, the cardiovascular risk prediction is made by a Deep neuro-fuzzy network (DNFN), such that the risks are classified into normal and hypertensive. Finally, the DNFN is trained using the developed Fractional Calculus-Horse Herd Optimization Algorithm (FCHOA), which is devised by combining Fractional Calculus (FC) and the Horse Herd Optimization algorithm (HOA). The proposed FCHOA-based DNFN offered enhanced efficiency with the highest accuracy, sensitivity and specificity of 91.6, 92.3 and 91.9%.