用改进的DenseNet201模型提升肺癌CT影像检测准确率
Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification
- 基于DenseNet201结合焦点损失与数据增强解决数据不平衡问题
- 在小样本且不平衡数据上实现98.95%的检测准确率
- 适合医学影像分析与肺癌早期筛查研究者参考
肺癌是全球男女中最常见的癌症之一，也是主要死亡原因之一。计算机断层扫描（CT）因其成本低、处理快，成为首选诊断方法。然而，现有基于CT图像的肺癌检测技术常出现大量假阳性，导致准确率偏低，根源在于数据集小且类别不平衡。本文提出一种基于DenseNet201的创新方法，融合焦点损失、数据增强与正则化技术，有效缓解数据不平衡与过拟合问题。实验结果表明，该方法表现优异，准确率达到98.95%。
原文摘要 · Abstract (English)
Lung cancer (LC) ranks among the most frequently diagnosed cancers and is one of the most common causes of death for men and women worldwide. Computed Tomography (CT) images are the most preferred diagnosis method because of their low cost and their faster processing times. Many researchers have proposed various ways of identifying lung cancer using CT images. However, such techniques suffer from significant false positives, leading to low accuracy. The fundamental reason results from employing a small and imbalanced dataset. This paper introduces an innovative approach for LC detection and classification from CT images based on the DenseNet201 model. Our approach comprises several advanced methods such as Focal Loss, data augmentation, and regularization to overcome the imbalanced data issue and overfitting challenge. The findings show the appropriateness of the proposal, attaining a promising performance of 98.95% accuracy.
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