Brain Stroke Detection Using 3D CNN

Brain strokes, deemed among the most perilous health conditions, present a growing threat globally, straining healthcare systems due to their high fatality rates. The brain, a three-pound marvel, orchestrates intelligence, sensory interpretation, movement initiation, and behavior regulation. Its intricate functions include thought, memory, touch, motor control, emotion, vision, and vital bodily processes. A stroke, delineated by cerebral circulation irregularities leading to brain cell demise, manifests as ischemic (arterial blockage) or hemorrhagic (vessel rupture). Data Augmentation is used to artificially increase the diversity of a training dataset by applying various transformations to the existing data. This study employs a 3D CNN model, enhancing image quality through preprocessing, to discern stroke presence using Computed Tomography Scan images. Impressively, the model achieves a 92.5% accuracy in identifying strokes, offering a promising tool for early detection and intervention, crucial in mitigating the severe consequences of this life-threatening condition.