A Novel Approach to Detect Stroke from 2D Images Using Deep Learning

Stroke is a disease that affects the arteries leading to and within the brain. Detecting stroke early and conveniently is much more difficult as there is no portable system to detect it. Most of the time the expensive diagnosis method of stroke is out of reach for low- and middle-income countries like ours. Hence, there is a significant necessity for an effective and labor-saving self-diagnosis platform. For the last few years, machine learning and deep learning are used to study medical-related information. Lately, deep learning has very quickly become transformative for health care, offering the ability to analyze data with a speed and much precision. This study gives an automated system to detect the stroke from prepossessed data using CNN and other deep learning models. The proposed methodology is to mainly classify the stroke person’s face from the normal or expressions face. For classification, we passed prepossessed stroke images for training, fed them into various deep architecture, and finally based on the classified expression, we classified normal and stroke patient. The experimental result shows that CNN classification model achieves accuracy 97.145% which is satisfactory. Overall, the aim of this study is to establish a fast and reliable system which will detect stroke on its early stage.