Abstract: Brain stroke prediction is a critical task in healthcare, as early detection can significantly improve patient outcomes. In this study, we propose an ensemble learning framework for brain stroke prediction using convolutional neural networks (CNNs) and pretrained deep learning models, specifically ResNet50 and DenseNet121. The ensemble model combines the strengths of these architectures to enhance predictive performance. Firstly, the CNN extracts relevant features from brain imaging data. Then, ResNet50 and DenseNet121, renowned for their efficacy in image classification tasks, further refine these features through deep learning-based feature extraction. The ensemble model integrates the predictions from these individual models to make a final prediction.
Keywords: Ensemble learning, Classification, CNN, Resnet50, DenseNet121
Cite:
Nandu Krishna G, Neha Mashoora, Nisar Ahamed P, Dr.Amirthavalli. M,"BRAIN STROKE PREDICTION USING ENSEMBLE LEARNING", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13350.
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DOI:
10.17148/IJARCCE.2024.13350
