Abstract

Accurate classification of brain tumors from magnetic resonance imaging (MRI) is essential for assisting clinical diagnosis and treatment planning. This study presents a deep learning–based approach for brain tumor classification using the EfficientNetB3 architecture. Transfer learning with initialization from weights learned on ImageNet is used, and the network is fine-tuned on a brain MRI dataset containing four classes: glioma, meningioma, pituitary tumor, and no tumor. The proposed system learns end to end to produce discriminative features from an image. Experimental results show that EfficientNetB3 achieves a test accuracy of 99%, with macro-averaged precision, recall (sensitivity), and F1-score of 99%. These results demonstrate the effectiveness of EfficientNetB3 for reliable and high-performance brain tumor classification.

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