标题： 用于准确检测和分类肺癌的高级深度学习技术 显示英文标题

标题： Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification

摘要： 肺癌（LC）是诊断最频繁的癌症之一，也是全球男女最常见的死亡原因之一。 计算机断层扫描（CT）图像由于其低成本和更快的处理时间，是最常用的诊断方法。 许多研究人员提出了使用CT图像识别肺癌的各种方法。 然而，这些技术存在显著的假阳性，导致准确性较低。 根本原因在于使用了小而不平衡的数据集。 本文介绍了一种基于DenseNet201模型的肺癌检测和分类创新方法。 我们的方法包括一些先进的方法，如焦点损失、数据增强和正则化，以克服数据不平衡问题和过拟合挑战。 研究结果表明该方案的适用性，达到了98.95%的高准确率。 显示英文摘要

摘要： 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.