Abstract: Stroke is an important health issue that affects millions of people globally each year. Early and precise stroke diagnosis is crucial for efficient treatment and better patient outcomes. Traditional stroke detection procedures, such as manual visual evaluation of clinical data, can be time-consuming and error-prone. Computer-aided diagnostic (CAD) technologies have emerged as a viable option for early stroke diagnosis in recent years. These systems analyze medical pictures, such as magnetic resonance imaging (MRI), and identify indicators of stroke using modern algorithms and machine learning approaches. The goal of this review paper is to offer a thorough overview of the current state-of-the-art in CAD systems for early stroke detection. We give an examination of the merits and limits of this technology, as well as future research and development directions in this field. Finally, we contend that CAD systems represent a promising solution for improving the efficiency and accuracy of early stroke diagnosis, resulting in better patient outcomes and lower healthcare costs.

Izzatul Husna Azman, Norhashimah Mohd Saad, Abdul Rahim Abdullah, Rostam Affendi Hamzah, Adam Samsudin and Shaarmila A/P Kandaya, “Automated CAD System for Early Stroke Diagnosis: Review” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140809

@article{Azman2023,
title = {Automated CAD System for Early Stroke Diagnosis: Review},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140809},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140809},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Izzatul Husna Azman and Norhashimah Mohd Saad and Abdul Rahim Abdullah and Rostam Affendi Hamzah and Adam Samsudin and Shaarmila A/P Kandaya}
}