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<p>Brain tumors present significant health challenges and necessitate early diagnosis due to their high mortality rates. Diagnosis through Magnetic Resonance Imaging (MRI) requires specialized expertise and remains susceptible to error. Consequently, the demand for automated diagnostic systems continues to grow. In response, this study proposes a novel Deep Learning (DL) model for brain tumor classification. A publicly available Figshare dataset containing 3064 T1-weighted contrast-enhanced brain MRI images representing three tumor types was used. The classification performance of fifteen DL architectures was initially evaluated to determine the most effective backbone. EfficientNetV2 demonstrated superior results and was selected for further development. An attention-based MLP-Mixer architecture was then integrated with EfficientNetV2 to improve classification performance. The final model’s performance was comprehensively compared with other DL models and established methods in the literature. Grad-CAM visualization was applied to interpret and validate the model’s decision-making process. The proposed model was evaluated using stratified five-fold cross-validation, achieving 98.53% accuracy, 98.37% precision, 98.48% recall, and 98.42% F1-score. These results demonstrate superior performance relative to previous studies. Additionally, Grad-CAM visualizations show that the model consistently focuses on relevant regions within MRI images, thereby improving interpretability and clinical reliability. The integration of EfficientNetV2 with an attention-based MLP-Mixer resulted in a robust DL model for clinical decision support systems, providing high accuracy and interpretability in brain tumor classification.</p>

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MRI-based brain tumor detection through an explainable EfficientNetV2 and MLP-mixer attention architecture

  • 期刊论文

  • 发表日期：

    2026年4月7日

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  • Mustafa Yurdakul,
  • Şakir Taşdemir

摘要

Brain tumors present significant health challenges and necessitate early diagnosis due to their high mortality rates. Diagnosis through Magnetic Resonance Imaging (MRI) requires specialized expertise and remains susceptible to error. Consequently, the demand for automated diagnostic systems continues to grow. In response, this study proposes a novel Deep Learning (DL) model for brain tumor classification. A publicly available Figshare dataset containing 3064 T1-weighted contrast-enhanced brain MRI images representing three tumor types was used. The classification performance of fifteen DL architectures was initially evaluated to determine the most effective backbone. EfficientNetV2 demonstrated superior results and was selected for further development. An attention-based MLP-Mixer architecture was then integrated with EfficientNetV2 to improve classification performance. The final model’s performance was comprehensively compared with other DL models and established methods in the literature. Grad-CAM visualization was applied to interpret and validate the model’s decision-making process. The proposed model was evaluated using stratified five-fold cross-validation, achieving 98.53% accuracy, 98.37% precision, 98.48% recall, and 98.42% F1-score. These results demonstrate superior performance relative to previous studies. Additionally, Grad-CAM visualizations show that the model consistently focuses on relevant regions within MRI images, thereby improving interpretability and clinical reliability. The integration of EfficientNetV2 with an attention-based MLP-Mixer resulted in a robust DL model for clinical decision support systems, providing high accuracy and interpretability in brain tumor classification.