A Diagnostic Model for Detection and Identification of Brain Stroke Using Convolutional Neural Network Algorithm

Stroke remains a leading cause of mortality and disability globally, and prompt identification is necessary to provide effective treatment. This paper explores new technologies, including machine learning-in particular, Convolutional neural network(CNN), advanced neuroimaging methods like computed tomography(CT), magnetic resonance image(MRI) and brain-computer interfaces (BCI), aid in the identification of strokes. The focus is on integrating non-invasive techniques to enable rapid diagnosis and personalized rehabilitation. Monitoring in actual time and adaptive algorithms enable the system to boost the prediction of stroke events. Advanced imaging methods and neural networks bridge the gaps in traditional diagnostics, enabling medical decision-making. This innovation provides clinicians with advanced tools for faster diagnoses and personalized rehabilitation plans, resulting in improved patient outcomes.