Abstract: Stroke remains one of the leading causes of death and long-term disability worldwide, whose impact imposes a heavy healthcare burden on individuals and systems. Early and accurate detection of stroke is critical in order to prevent delays in seeking medical care, provide improved patient outcomes, and prevent complications. The application of CT and MRI scans, the traditional methods, is not only required to be interpreted by an expert but is also time-consuming and prone to variation among radiologists. Growth in artificial intelligence (AI), and increasingly in machine learning (ML) and deep learning (DL), offers promising avenues for enhancing the accuracy and speed of stroke detection. This paper provides a detailed overview of ML and DL techniques applied in brain stroke detection, detailing the methodologies, the prerequisites for application, and the challenges posed. It explains various image processing techniques and classifying algorithms intended for detecting and segmenting regions affected by strokes within brain scans. We also cover developing an AI-based system integrating image processing with ML algorithms for assisting medical professionals to diagnose strokes more effectively.
Through an extensive review of the literature, the current work presents the most recent advances in AI-based stroke detection, considering both supervised and unsupervised learning approaches, feature extraction methods, model performance evaluation measures, and challenges regarding dataset access, model interpretability, computational intensity, and deployment in the real world.
By synthesizing current research evidence, this paper aims to enlighten the emerging role of AI in stroke detection and diagnosis. It also offers future directions for research aimed at improving model generalization, developing explainable AI models, and integrating AI tools into clinical practice. The evidence provided contributes to the continuum of initiatives towards stroke diagnosis improvement through novel technological advancements, leading to improved patient care and outcomes.
Keywords: Stroke detection, Machine learning, Deep learning, Artificial intelligence, Medical imaging, Image processing, Neural networks, CT scan, MRI, Stroke classification, Healthcare technology.
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DOI:
10.17148/IJARCCE.2025.14598
