Identifying Brain Strokes by Image Analysis Using Transfer Learning

Brain Stroke is a life-threatening medical condition that requires prompt diagnosis and treatment to minimize its devastating effects like partial paralysis, speech impairment, and memory loss. The classification of brain strokes is crucial for accurate and timely medical treatment. Our research employs transfer learning, such as Convolutional Neural Networks (CNN), ResNet, RadiNet, and MobileNet algorithms, to classify brain strokes from Computed Tomography (CT) scans, focusing on improving both the speed and accuracy of stroke diagnosis using transfer learning techniques. To deal with the unbalanced nature of the dataset, we implemented various features of data augmentation techniques and achieved class balance in the dataset. Our proposed model MobileNet on augmented down-sampling dataset demonstrates, exceptional overall F1-scores achieving up to 99%, precision of 99% and recall of 99 % for no brain stroke cases and F1-score of 99%, precision of 99%, and recall of 100% for stroke cases, with an overall accuracy of 99 %. We conclude that our research adds a valuable and efficient method in classifying a CT scan into a stroke or no brain stroke case, which is still an open problem despite the usage of deep learning and machine learning techniques.