Stroke is one of the leading causes of mortality and long-term disability worldwide, making early and accurate diag-nosis essential for effective clinical intervention. Brain imaging modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) play a crucial role in stroke detection and assessment. However, manual interpretation of medical images is often challenging due to subtle lesion characteristics, image noise, and inter-patient variability, which may lead to delayed or inconsistent diagnosis. Recent advances in artificial intelligence, particularly machine learning and deep learning techniques, have significantly improved the automated analysis of medical images for stroke detection and classification.

This paper presents a comprehensive survey of existing artifi-cial intelligence–based approaches for automated stroke diagnosis from brain imaging data. The survey reviews various method-ologies including traditional machine learning techniques, con-volutional neural network (CNN) architectures, transfer learn-ing models, hybrid frameworks combining deep learning with optimization algorithms, and ensemble learning approaches. In addition, dimensionality reduction techniques, feature extraction strategies, and classification models commonly used in stroke detection systems are analyzed and compared. The survey also examines the growing role of explainable artificial intelligence (XAI) methods such as Grad-CAM and SHAP in improving the interpretability and clinical reliability of AI-based diagnostic systems.

Furthermore, this study reviews publicly available datasets, evaluation metrics, and performance benchmarks used in the lit-erature, highlighting current challenges such as data imbalance, model generalization, computational complexity, and clinical deployment limitations. Finally, future research directions are discussed, including lightweight models for real-time applications, integration with clinical decision-support systems, and improved interpretability for healthcare adoption. This survey aims to provide researchers and healthcare technologists with a struc-tured overview of the current state of AI-driven stroke detection methods and emerging opportunities for developing reliable and clinically applicable diagnostic solutions.

Index Terms—Stroke detection, brain imaging, computed to-mography (CT), machine learning, deep learning, convolutional neural networks, transfer learning, explainable artificial intelli-gence, medical image analysis.