改进ResNet34模型，实现高精度低参数的脑肿瘤图像分类
Research on Brain Tumor Classification Method Based on Improved ResNet34 Network
- 采用多尺度输入与Inception v2模块增强特征提取能力
- 五折交叉验证下准确率达98.8%，比原模型高1%且参数量仅为其80%
- 适合医学影像分析、轻量化模型设计的研究者参考
以往放射科图像解读主要依赖人工，耗时且效率低。即使使用浅层卷积神经网络，分类准确率仍不理想。为提升脑肿瘤医学图像分类的效率与准确性，本文提出一种基于改进ResNet34网络的分类模型。该模型以ResNet34为骨干网络，引入多尺度输入模块作为第一层，并在残差下采样层中嵌入Inception v2模块。此外，通过通道注意力机制从通道维度为不同特征分配权重，强化关键信息。五折交叉验证结果显示，改进模型平均分类准确率约为98.8%，较ResNet34提升1个百分点，同时参数量仅为原模型的80%。因此，该模型在保证更高精度的同时显著降低模型复杂度，实现高效精准的分类效果。
原文摘要 · Abstract (English)
Previously, image interpretation in radiology relied heavily on manual methods. However, manual classification of brain tumor medical images is time-consuming and labor-intensive. Even with shallow convolutional neural network models, the accuracy is not ideal. To improve the efficiency and accuracy of brain tumor image classification, this paper proposes a brain tumor classification model based on an improved ResNet34 network. This model uses the ResNet34 residual network as the backbone network and incorporates multi-scale feature extraction. It uses a multi-scale input module as the first layer of the ResNet34 network and an Inception v2 module as the residual downsampling layer. Furthermore, a channel attention mechanism module assigns different weights to different channels of the image from a channel domain perspective, obtaining more important feature information. The results after a five-fold crossover experiment show that the average classification accuracy of the improved network model is approximately 98.8%, which is not only 1% higher than ResNet34, but also only 80% of the number of parameters of the original model. Therefore, the improved network model not only improves accuracy but also reduces clutter, achieving a classification effect with fewer parameters and higher accuracy.
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