Advancing Brain Stroke Detection and Classification with a Customized Multi-Layered Deep Neural Network

This paper proposes a new approach for efficient detection and classification of brain strokes using medical image processing based on computational intelligence. Diagnosis of brain stroke is still a challenging task that often requires much human intervention to provide reliable results. A deep neural network with a model inspired by AlexNet was developed to address this challenge, and it has so far shown remarkable capability in the identification of stroke patients and further classification into ischemic and hemorrhagic subtypes. In order to increase generalization and avoid overfitting, the study involves thorough data gathering and preparation, with medical pictures being properly classified, standardized, and improved via data augmentation and class weight balancing. The proposed method performed better than 94% for the detection of stroke and 98% for subtype classification, with remarkable accuracy. These results offer a reliable, fast diagnostic tool that may improve patient outcomes and accelerate clinical processes, hence constituting a significant advance in medical imaging and AI.