用迁移学习提升肺癌检测精度，准确率达98%
Hybrid deep convolution model for lung cancer detection with transfer learning
- 融合迁移学习的混合卷积模型，优化敏感度与特异度
- 在测试中达到98%准确率、97%敏感度，误报极少
- 可生成热力图定位病灶，适合医学影像辅助诊断
医疗研究的进步显著提升了我们对疾病机制、诊断精度和治疗方案的理解。然而，由于早期和准确诊断存在挑战，肺癌仍是全球癌症相关死亡的主要原因。尽管现有肺癌检测模型展现出潜力，仍存在进一步提升准确性的空间。为此，本文提出一种基于迁移学习的混合深度卷积模型——最大敏感度神经网络（MSNN），旨在通过优化敏感度和特异度来提高肺癌检测精度。实验验证表明，该模型优于现有深度学习方法，在测试中实现98%的准确率和97%的敏感度。通过将敏感度图叠加到肺部计算机断层扫描（CT）图像上，可直观呈现最具恶性或良性特征的区域。该方法在减少误报的同时显著提升了诊断准确性。
原文摘要 · Abstract (English)
Advances in healthcare research have significantly enhanced our understanding of disease mechanisms, diagnostic precision, and therapeutic options. Yet, lung cancer remains one of the leading causes of cancer-related mortality worldwide due to challenges in early and accurate diagnosis. While current lung cancer detection models show promise, there is considerable potential for further improving the accuracy for timely intervention. To address this challenge, we introduce a hybrid deep convolution model leveraging transfer learning, named the Maximum Sensitivity Neural Network (MSNN). MSNN is designed to improve the precision of lung cancer detection by refining sensitivity and specificity. This model has surpassed existing deep learning approaches through experimental validation, achieving an accuracy of 98% and a sensitivity of 97%. By overlaying sensitivity maps onto lung Computed Tomography (CT) scans, it enables the visualization of regions most indicative of malignant or benign classifications. This innovative method demonstrates exceptional performance in distinguishing lung cancer with minimal false positives, thereby enhancing the accuracy of medical diagnoses.
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