用注意力机制提升乳腺癌病理图像分类准确率
Breast Cancer Histopathology Classification using CBAM-EfficientNetV2 with Transfer Learning
- 在EfficientNetV2中加入CBAM注意力模块，聚焦关键组织区域
- 在400倍放大下达到99.01%准确率和98.31%F1分数
- 适合临床实时诊断，兼顾精度与计算效率
乳腺癌病理图像分类对早期发现和改善患者预后至关重要。本研究提出一种新方法，利用EfficientNetV2模型增强特征提取并关注相关组织区域。所提模型在BreakHis数据集上于多个放大倍数（40X、100X、200X、400X）下进行评估。其中，集成CBAM的EfficientNetV2-XL在400X放大下表现最优，准确率达99.01%，F1分数达98.31%，优于现有先进方法。通过引入对比度受限自适应直方图均衡化（CLAHE）进行预处理，并优化计算效率，该方法展现出良好的实时临床部署潜力。结果表明，注意力增强的可扩展架构在提升乳腺癌诊断精度方面具有显著前景。
原文摘要 · Abstract (English)
Breast cancer histopathology image classification is critical for early detection and improved patient outcomes. 1 This study introduces a novel approach leveraging EfficientNetV2 models, to improve feature extraction and focus on relevant tissue regions. The proposed models were evaluated on the BreakHis dataset across multiple magnification scales (40X, 100X, 200X, and 400X). 2 Among them, the EfficientNetV2-XL with CBAM achieved outstanding performance, reaching a peak accuracy of 99.01 percent and an F1-score of 98.31 percent at 400X magnification, outperforming state-of-the-art methods. 3 By integrating Contrast Limited Adaptive Histogram Equalization (CLAHE) for preprocessing and optimizing computational efficiency, this method demonstrates its suitability for real-time clinical deployment. 3 The results underscore the potential of attention-enhanced scalable architectures in advancing diagnostic precision for breast cancer detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。