Robust Brain Strokes Diagnosis in the CT Images into Several Categories Using Deep Machine Learning Models

Brain stroke represents the main contributor to both disability and mortality in the world. The precise and immediate detection of this disease is crucial to save life of patients and enable effective intervention. A deep learning framework is developed in this research to detect and diagnosis brain stroke into three main categories: normal, ischemic and hemorrhagic. Four pre-trained deep learning models are leveraged in this study including VGG19, CNN, EfficientNetB0 and ResNeXt-50_32x4d. An augmentation and pre-processing techniques are utilized in this framework to reduce class imbalance and unify wide imaging data, hence improving model generalization. Evaluation on the a well curated dataset reveals perfect performance especially precision and recall metrics with scores of (99%) among different stroke types. ResNeXt-50_32x4d model demonstrates the best performance due its strong and robust architecture. Next is the EfficientNetB0 delivers also good performance for its efficient architecture and a smaller number of parameters. Overall, the evaluation analysis results of diagnosing brain stroke types refer to the superiority of the proposed approach in comparison with the baseline deep learning methods. This study also overcomes challenges such as false negatives in the diagnosis of early ischemic cases and false positives arising from anatomical variations, suggesting solutions that integrate several AI models. To sum up, this framework demonstrates encouraging pathway to enhance diagnosis of brain strokes by efficient, automated and interpretable AI tools.