Brain Stroke detection Using AI/ML

Brain stroke is a leading cause of disability and mortality worldwide, requiring rapid and accurate diagnosis for effective treatment. Traditional stroke diagnosis methods, such as CT and MRI scans, often rely on expert interpretation, which can be time-consuming and prone to human error. This study explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance the accuracy and efficiency of brain stroke detection. We propose a deep learning-based model that utilizes medical imaging data, such as CT and MRI scans, to automatically detect stroke occurrences. The model is trained on a dataset of annotated stroke images and leverages convolutional neural networks (CNNs) for feature extraction and classification. Additionally, we explore machine learning algorithms such as Random Forest, Support Vector Machines (SVM), and XGBoost for predictive stroke risk assessment based on clinical and demographic data. Our results demonstrate that AI-driven stroke detection can achieve high accuracy and assist healthcare professionals in early diagnosis, leading to improved patient outcomes. The study also highlights the potential for real-time stroke detection using AI-powered cloud platforms and mobile applications. Future work will focus on optimizing model performance, integrating multi-modal data sources, and enhancing explainability for clinical adoption.