Early detection of stroke using MRI images - A machine learning approach

Health is paramount in achieving overall well-being and happiness. Detecting strokes at their earliest stages is crucial for limiting brain damage and minimizing long-term consequences. Medical imaging, particularly in the form of Magnetic Resonance Imaging (MRI) technology, has emerged as a powerful tool in the field of healthcare. However, manually identifying and locating ischemic strokes in MRI images can be time-consuming and challenging. This research aims to develop a machine learning-based approach for the automatic detection of strokes including ischemic strokes, utilizing cutting-edge methods for identification and classification. The proposed methodology consists of six key stages that collectively form a comprehensive procedure. To enhance the quality of MRI images, Gabor filters are employed following preprocessing based on functional criteria. Additionally, an image enhancement technique known as adaptive histogram equalization (AHE) is implemented. For the prediction of strokes, Convolutional Neural Networks (CNNs) are employed. Experimental results demonstrate that the developed model achieves an accuracy above 90%. The approach showcased in this research is both simple and effective, offering a productive solution for the early detection of ischemic strokes. By leveraging the power of machine learning and advanced imaging techniques, this research paves the way for improved stroke diagnosis and timely intervention, leading to enhanced patient outcomes and quality of life.