Traditional diagnostic approaches are time-consuming and labor-intensive, and the field currently lacks a comprehensive evaluation of mainstream models that addresses their complementary strengths. Computer-aided diagnosis methods can significantly increase the accuracy and efficiency of MRI-based image classification, thereby providing useful support for brain tumor computer-aided diagnosis research. To address these challenges, this study utilizes a brain tumor MRI dataset comprising 4,264 images from 333 subjects to conduct a comprehensive evaluation of various mainstream Convolutional Neural Networks and Vision Transformers under a repeated patient-level validation protocol. In addition, representative Transformer models are further evaluated under a pretrained partial fine-tuning setting. The results reveal that DenseNet-121 provides a favorable balance between diagnostic performance and computational efficiency under the no-external-pretraining setting, whereas pretrained Transformer models remain competitive when external visual pretraining is available. Building upon these findings, we propose CAHA-Net (<Emphasis Type="BoldUnderline">C</Emphasis>oordinate <Emphasis Type="BoldUnderline">A</Emphasis>ttention and <Emphasis Type="BoldUnderline">H</Emphasis>ybrid <Emphasis Type="BoldUnderline">A</Emphasis>ugmentation Enhanced DenseNet), an improved architecture based on DenseNet-121. This method employs a hybrid augmentation strategy combining Contrast Limited Adaptive Histogram Equalization and FMix, aiming to enhance local contrast and increase tumor-background variation during training. Furthermore, by integrating a Coordinate Attention mechanism, the model simultaneously captures spatial positional information in both the horizontal and vertical directions, promoting more lesion-sensitive feature representations during classification. The experimental results demonstrate that CAHA-Net achieves the best average performance among the tested ablation variants in multi-class classification tasks, achieving a Macro-F1 score of 94.14% ± 1.16%, an AUC of 98.97% ± 0.48%, and a false positive rate as low as 1.66% ± 0.39%. The systematic ablation results indicate that FMix is the main independent contributor to performance improvement, whereas coordinate attention helps stabilize feature representation within the complete augmentation-attention pipeline. The proposed model provides preliminary evidence of robustness and cross-dataset transferability, offering an efficient and useful reference for computer-aided diagnostic research on brain tumors.

CAHA-Net: A novel MR image classification model based on DenseNet incorporating coordinate attention and hybrid augmentation

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2026年5月28日

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Hui Qi,
Ruizhe Guo,
Jiajun He,
Xiyi Wang,
Yaling Han,
Xuchu Jiang,
Chibiao Liu

摘要

Traditional diagnostic approaches are time-consuming and labor-intensive, and the field currently lacks a comprehensive evaluation of mainstream models that addresses their complementary strengths. Computer-aided diagnosis methods can significantly increase the accuracy and efficiency of MRI-based image classification, thereby providing useful support for brain tumor computer-aided diagnosis research. To address these challenges, this study utilizes a brain tumor MRI dataset comprising 4,264 images from 333 subjects to conduct a comprehensive evaluation of various mainstream Convolutional Neural Networks and Vision Transformers under a repeated patient-level validation protocol. In addition, representative Transformer models are further evaluated under a pretrained partial fine-tuning setting. The results reveal that DenseNet-121 provides a favorable balance between diagnostic performance and computational efficiency under the no-external-pretraining setting, whereas pretrained Transformer models remain competitive when external visual pretraining is available. Building upon these findings, we propose CAHA-Net (Coordinate Attention and Hybrid Augmentation Enhanced DenseNet), an improved architecture based on DenseNet-121. This method employs a hybrid augmentation strategy combining Contrast Limited Adaptive Histogram Equalization and FMix, aiming to enhance local contrast and increase tumor-background variation during training. Furthermore, by integrating a Coordinate Attention mechanism, the model simultaneously captures spatial positional information in both the horizontal and vertical directions, promoting more lesion-sensitive feature representations during classification. The experimental results demonstrate that CAHA-Net achieves the best average performance among the tested ablation variants in multi-class classification tasks, achieving a Macro-F1 score of 94.14% ± 1.16%, an AUC of 98.97% ± 0.48%, and a false positive rate as low as 1.66% ± 0.39%. The systematic ablation results indicate that FMix is the main independent contributor to performance improvement, whereas coordinate attention helps stabilize feature representation within the complete augmentation-attention pipeline. The proposed model provides preliminary evidence of robustness and cross-dataset transferability, offering an efficient and useful reference for computer-aided diagnostic research on brain tumors.