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What is Deep Learning for Brain Tumor Detection about?

This study presents a deep learning-based approach for brain tumor detection from MRI scans, evaluating three architectures: CNN, MobileNet, and Xception, on a dataset of 7770 images. The Xception model achieved the highest accuracy of 98%, outperforming the other models, which highlights its potential for clinical applications in neuroimaging. Future work will focus on optimizing model parameters and integrating clinical data to enhance diagnostic accuracy and generalization.

Author

21e51a6960

Language

EN