Advanced Attention-Based Pre-Trained Transfer Learning Model for Accurate Brain Tumor Detection and Classification from MRI Images

Brain tumor identification using MRI images involves the detailed examination of brain tissues to detect and characterize tumors. Conventional ML and DL algorithms sometimes encounter difficulties due to a lack of labelled data, resulting in inferior performance and poor generalization. To address these issues, this study introduces an Advanced Attention-based Pre-trained Transfer Learning (TL) model that enhances accuracy and resilience in identifying and categorizing brain tumors using MRI images. The methodology starts with pre-processing, which includes image scaling and noise reduction with an adaptive median filter. After pre-processing, the images are fed into a CNN-based framework called Pre-trained Attention-fused Image SpectraNet. This framework comprises of five convolutional layers, after which Rectified Linear Unit (ReLU) activation and pooling layers are added to learn progressively more complex features. A novel self-attention layer is implemented to capture deep features that reveal aberrant tissue patterns, hence increasing model interpretability and accuracy. A globally average pooling layer is employed to reduce computational complexity, and it is accompanied by a fully connected layer with batch normalization to assure stability and convergence during training. The last layer uses softmax to categorize normal, pituitary, glioma, and meningioma. Utilizing the Adam optimizer, the suggested approach enhances performance, yielding excellent metrics such as 98.33% accuracy, 98.35% precision, 98.28% recall, and a 98.31% F1-score. These measures show considerable increases over existing ML and DL methods, demonstrating the system's ability to improve brain tumor detection accuracy. The advancement of these treatments has significant implications for medical professionals who specialize in the timely identification of brain tumors.