A DEEP LEARNING MODEL FOR BRAIN STROKE DETECTION USING CT SCANS

—Brain stroke is a serious neurological condition that requires quick and accurate diagnosis to reduce the risk of death and long-term disability. Computed Tomography (CT) scans are commonly used for stroke detection because they are fast and easily available. However, analyzing these images manually can take time and may vary between radiologists. This study presents a deep learning–based system for detecting brain stroke using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. The CNN model helps in extracting important features from CT images, while the LSTM model enhances the classification process. The results show high accuracy and reliability, highlighting the potential of deep learning in supporting medical image analysis and improving clinical decision-making.