Kalkan Muruvvet, Guzel Mehmet S, Ekinci Fatih, Akcapinar Sezer Ebru, Asuroglu Tunc
Department of Computer Engineering, Ankara University, 06830 Ankara, Turkey.
Department of Institute of Nuclear Sciences, Ankara University, 06100 Ankara, Turkey.
Cancers (Basel). 2024 Sep 28;16(19):3321. doi: 10.3390/cancers16193321.
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.
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.
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%.
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.
肺癌是全球癌症相关死亡的主要原因，在男性中排名第一，在女性中排名第二。由于其侵袭性，肿瘤的早期检测和精确定位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，利用CT扫描图像在肺癌早期阶段进行识别。
使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，使用诸如UNet、SegNet和InceptionUNet等模型对肿瘤区域进行分割。
InceptionResNetV2模型实现了最高检测准确率98.5%，而UNet产生了最佳分割结果，杰卡德指数为95.3%。
该研究证明了深度学习模型，特别是InceptionResNetV2和UNet，在检测和分割肺癌方面的有效性，显示出在辅助早期诊断和治疗方面的巨大潜力。未来的工作可以集中在优化这些模型并探索它们在其他医学领域的应用。
