Unlocking the Future of Stroke Diagnosis-Bayesian CNN and MRI Fusion

Stroke, as a life-threatening medical condition, necessitates rapid and accurate diagnosis to ensure timely treatment and optimal patient outcomes. In addressing the complex task of identifying strokes within Magnetic Resonance Imaging (MRI) images, a Bayesian Convolutional Neural Network is introduced. This paper addressing the urgent demand for enhanced stroke detection in medical imaging. By harnessing the Bayesian framework, Convolutional Neural Network (CNN) achieves a notable accuracy rate of 92.98%, underscoring its effectiveness in delivering precise diagnoses. The approach distinguishes itself by its ability to measure prediction uncertainty, a critical element frequently disregarded in conventional models. This capability equips healthcare professionals with valuable information regarding the model's confidence in its stroke predictions, thereby enhancing clinical decision-making and ultimately elevating the standard of patient care in the context of stroke management.