Image-Based Coronary Artery Disease Diagnosis Using Differential Evolution and Texture Analysis - Pawar, Prashant S.; Shelke, Abhijeet B.; Chandran S., Nisha; Singh, Anupam (International Journal of Intelligent Systems and Applications in Engineering, 2023)
The critical need for precise and effective diagnostic procedures is highlighted by the fact that coronary artery disease (CAD) continues to be a leading cause of mortality globally. In this article, we present a novel method for coronary artery disease (CAD) diagnosis using textural analysis and differential evolution (DE) optimisation on coronary artery pictures. The combination of TA, a potent image processing method, with DE, a reliable global optimisation algorithm, shows promising results in improving the precision and dependability of CAD diagnosis.The proposed procedure starts with the acquisition of coronary artery pictures, which are often made possible by non-invasive methods like computed tomography angiography or coronary angiography. To improve quality and lower noise, these pictures have undergone pre-processing. Then, using DE, a subset of pertinent texture features is chosen, improving the recognition of CAD-related patterns. The accuracy of diagnostics is improved while computational complexity is greatly reduced by this feature selection approach.Then, using texture analysis on the features that have been chosen, the coronary artery images are used to derive unique textural patterns and statistical properties. Following that, a machine learning model for CAD classification, such as a support vector machine or deep neural network, is trained using these textural features. Our tests show that DE-based feature selection, followed by texture analysis, performs better than conventional CAD diagnosis techniques, obtaining a greater level of sensitivity and specificity.The outcomes of a thorough analysis of a wide range of coronary artery pictures demonstrate the potential of our method to improve CAD diagnosis. We provide a contribution to the creation of a more precise and effective CAD diagnostic tool by integrating DE optimization with TA, which may help clinicians identify diseases earlier and plan treatments. This study paves the path for more accurate image-based CAD diagnoses, better patient outcomes, and lower healthcare expenditures.