文章：

基于CT图像的肺癌分型深度学习方法的比较分析

Comparative Analysis of Deep Learning Methods on CT Images for Lung Cancer Specification

原文发布日期：28 September 2024

DOI: 10.3390/cancers16193321

类型: Article

开放获取: 是

英文摘要：

Background: Lung cancer is the leading cause of cancer-related deaths worldwide, ranking first in men and second in women. Due to its aggressive nature, early detection and accurate localization of tumors are crucial for improving patient outcomes. This study aims to apply advanced deep learning techniques to identify lung cancer in its early stages using CT scan images. Methods: Pre-trained convolutional neural networks (CNNs), including MobileNetV2, ResNet152V2, InceptionResNetV2, Xception, VGG-19, and InceptionV3, were used for lung cancer detection. Once the disease was identified, the tumor’s region was segmented using models such as UNet, SegNet, and InceptionUNet. Results: The InceptionResNetV2 model achieved the highest detection accuracy of 98.5%, while UNet produced the best segmentation results, with a Jaccard index of 95.3%. Conclusions: The study demonstrates the effectiveness of deep learning models, particularly InceptionResNetV2 and UNet, in both detecting and segmenting lung cancer, showing significant potential for aiding early diagnosis and treatment. Future work could focus on refining these models and exploring their application in other medical domains.

摘要翻译： 

背景：肺癌是全球癌症相关死亡的主要原因，在男性中居首位，在女性中居第二位。由于其侵袭性，早期检测和准确定位肿瘤对于改善患者预后至关重要。本研究旨在应用先进的深度学习技术，通过CT扫描图像识别早期肺癌。方法：采用预训练的卷积神经网络（CNN），包括MobileNetV2、ResNet152V2、InceptionResNetV2、Xception、VGG-19和InceptionV3进行肺癌检测。一旦识别出疾病，使用UNet、SegNet和InceptionUNet等模型对肿瘤区域进行分割。结果：InceptionResNetV2模型实现了最高的检测准确率，达到98.5%，而UNet在分割方面表现最佳，其Jaccard指数为95.3%。结论：本研究证明了深度学习模型，特别是InceptionResNetV2和UNet，在肺癌检测和分割中的有效性，显示出在辅助早期诊断和治疗方面的巨大潜力。未来的工作可以侧重于优化这些模型，并探索其在其他医学领域的应用。

原文链接：

Comparative Analysis of Deep Learning Methods on CT Images for Lung Cancer Specification