A Hybrid Brain Tumor Classfication Study Based on CBAM and EfficientNet with Improved Channel Attention

Abstract

Abstract: In order to further improve the accuracy and robustness of brain tumor image diagnosis， a novel hybrid brain tumor classification method based on CBAM（Convolutional Block Attention Module） and EfficientNet with improved channel attention mechanism （IC+IEffxNet） is proposed. The method is divided into 2 stages. In the first stage， the features will be extracted by CBAM model based on improved spatial attention mechanism. In the second stage， the sequence and exception （SE） block in EfficientNet architecture is replaced by the efficient channel attention （ECA） block， and the combined feature output of the first stage is used as the input of the second stage. Experiment shows the 4 classifications of glioma， meningioma， pituitary and normal images from the mixed brain tumor MRI dataset. The results show that the average classification accuracy is about 0.5~2 percentage points higher than the existing methods. The experimental results demonstrate the effectiveness of the method and provide a new reference for medical experts to accurately judge brain tumor.

Key words: brain tumor, ECA, EfficientNet, CBAM, classification

HUA Xin-yu, QI Yun-song. A Hybrid Brain Tumor Classfication Study Based on CBAM and EfficientNet with Improved Channel Attention[J]. Computer and Modernization, 2023, 0(05): 1-7.

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A Hybrid Brain Tumor Classfication Study Based on CBAM and EfficientNet with Improved Channel Attention

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