Image Classification of Stroke Blood Clot Origin

Abstract

The field of computer vision is constantly expanding and evolving, and it has seen tremendous growth in recent years. Computer vision includes image classification as a fundamental component. The critical components for making the best decisions are image categorization and interpretation. This study intends to examine several etiology clots labels, such as Cardiac Embolic and Large Artery Atherosclerosis (CE & LAA), for researchers and practitioners of medical image analysis (particularly of blood clot origin). An analysis of the accuracy and processing speed of various image classification methods using neural network topologies. This report also describes the available medical data set and explains the performance measures of the techniques that are currently accessible. Some of the Deep Learning architectures, including CNN, VGG-16, Efficient-Net, and Res-Net, are studied in the article and discuss the trends with challenges in the application of medical image analysis.