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This dissertation presents a novel approach for brain tumor classification using Data-Efficient Image Transformers (DeiT), addressing the limitations of traditional Convolutional Neural Networks (CNNs). The proposed model achieved a classification accuracy of 98% and a rapid identification time of approximately 0.5 seconds, making it suitable for real-world clinical applications. The research emphasizes the importance of efficiency, precision, and scalability in medical imaging, paving the way for future in

Author

dosaddeepak01

Language

EN