Advanced Stroke Prediction Leveraging MRI Data with an Ensemble Convolutional Neural Network Framework

Brain stroke is a critical condition which should be diagnosed early if fatal consequences are to be avoided. Conventional diagnosis of strokes is based on the manual scanning of MRIs, which is tedious and susceptible to human error. In this work, we present a deep-learning-based stroke prediction model that relies on two machine-learning techniques: ensemble learning and convolutional neural networks. The proposed method analyzes with equal efficacy 2D and 3D MRIs using leading-edge architectures including U-Net, VGG16, and DenseNet121. The model ensures higher accuracy and robustness through transfer learning and data augmentation methods, hence it is equipped to operate efficiently even with very few labeled datasets. Through the ensemble technique amalgamating the outputs from individual models, the accuracy of stroke classification got a major boost in the system. This research further opens avenues for a computationally efficient, scalable, and automated stroke prediction system and underscores the importance of AI in medical imaging. Results show the proposed model can aid medical practitioners bring AI developments closer to real-world healthcare applications by fast-tracking diagnosis and enhancing clinical decision-making.