对比五种深度模型，EfficientNetB0在脑瘤分类中表现最佳。
Multi-Class Brain Tumor Classification Using Advanced Deep Learning Models: A Comparative Study
- 统一框架下测试五种CNN模型，评估多类脑瘤分类性能。
- EfficientNetB0准确率达95%，对难检测的脑膜瘤召回率达89%。
- 模型效率比深度更重要，适合医疗影像诊断场景使用。
尽管深度学习取得进展，从MRI图像准确分类脑瘤仍具挑战。本研究对五种卷积神经网络架构（包括定制基线模型及四种预训练模型）进行综合评估，使用约10,000张临床来源MRI图像数据集，开展多类脑瘤分类。所用模型包括VGG16、VGG19、DenseNet121和EfficientNetB0，均在相同实验框架下测试与训练。性能通过整体准确率和肿瘤类别召回率衡量，以反映临床相关性。结果表明，EfficientNetB0整体分类准确率最高（95%），优于VGG16（94.37%）、VGG19（92.29%）、DenseNet121（90.91%）和定制CNN（78.00%）。尤其值得注意的是，对难以检测的脑膜瘤，简单CNN召回率仅约20%，而EfficientNetB0提升至89%。此外，深层的VGG19表现反而劣于浅层的VGG16，提示在医学图像任务中模型效率可能比深度更重要。总体而言，EfficientNetB0在准确率、参数量与临床性能间实现最优平衡。
原文摘要 · Abstract (English)
Despite recent advancements in deep learning, accurately classifying brain tumors from MRI images continues to pose challenges. In this research, we present a comprehensive evaluation of five different convolutional neural networks (CNN) architectures, including a customized baseline model and four pre-trained models - for use in classifying multi-class brain tumors using a clinically-sourced dataset of approximately 10,000 MRI images. We have utilized five different architectures; VGG16, VGG19, DenseNet121, and EfficientNetB0, which were all tested and trained within an identical experimental framework. Performance was measured by both overall accuracy and tumor-wise recall as a means to measure the clinically-relevant performance of each architecture. We found that EfficientNetB0 had the best overall classification accuracy at 95%, when compared to the other architectures tested; specifically VGG16 (94.37%), VGG19 (92.29%), DenseNet121 (90.91%) and the customized CNN (78.00%). An especially important finding of our research was the considerable improvement in detecting meningiomas; specifically, while simple CNNs could detect meningiomas with a recall rate of approximately 20%, EfficientNetB0 was able to detect meningiomas with a recall rate of 89%. Meningiomas are often difficult to detect because they can appear very subtly on MRI images. Additionally, an interesting finding was that the deeper VGG19 performed worse than the shallower VGG16. This indicates that in many cases the architectural efficiency of a CNN model may be more important than its depth when working with medical images. Overall, EfficientNetB0 appears to provide the optimal trade-off between classification accuracy, number of parameters used in the model and clinically meaningful performance.
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