A Lightweight Attention-Enhanced and Rotation-Invariant EfficientNet Framework for Accurate Brain Tumor Classification from MRI Images

Abstract

Magnetic resonance imaging (MRI) brain tumor classification is an important factor in the early diagnosis and plans of treatment. Nevertheless, the current methods of deep learning lack robustness to spatial variations, lack attention to tumor-relevant features, and high computational complexity, which limits their use in the real world. To solve these issues, the paper provides a proposal of DA-CBAM EfficientNet which is a rotation-augmented and compressed transfer learning architecture to achieve precise and efficient brain tumor classification. The given model relies on an EfficientNet backbone, which applies the transfer learning to utilize the available pretrained representations and adjust the domain specificities of MRI. Data augmentation is done in a systematic manner by using rotation-based to provide orientation-invariance learning and better generalization. The Convolutional Block Attention Module (CBAM) and a Dynamic Attention-Weighted Enhancement Function (DAWEF) are used to gain a better understanding of informative feature channels and spatial locations related to tumors that can be enhanced by learning a discriminative feature. To achieve feasibility of deployment, pruning and quantization are done in a structured way to reduce the size of the model and inference latency by a fair margin without affecting the classification performance. Extensive experimental testing of an experimental dataset of a benchmark brain MRI shows that the proposed DA-CBAM EfficientNet will always be the best among the current state-of-the-art approaches in terms of accuracy, robustness, and computational efficiency. The findings validate the adequacy of the proposed framework to real time clinical decision support and resource limited healthcare settings.