Ahmed Imran, Chehri Abdellah, Jeon Gwanggil, Piccialli Francesco
IEEE/ACM Trans Comput Biol Bioinform. 2023 Jul-Aug;20(4):2445-2456. doi: 10.1109/TCBB.2022.3192139. Epub 2023 Aug 9.
Recent advancement in biomedical imaging technologies has contributed to tremendous opportunities for the health care sector and the biomedical community. However, collecting, measuring, and analyzing large volumes of health-related data like images is a laborious and time-consuming job for medical experts. Thus, in this regard, artificial intelligence applications (including machine and deep learning systems) help in the early diagnosis of various contagious/ cancerous diseases such as lung cancer. As lung or pulmonary cancer may have no apparent or clear initial symptoms, it is essential to develop and promote a Computer Aided Detection (CAD) system that can support medical experts in classifying and detecting lung nodules at early stages. Therefore, in this article, we analyze the problem of lung cancer diagnosis by classification and detecting pulmonary nodules, i.e., benign and malignant, in CT images. To achieve this objective, an automated deep learning based system is introduced for classifying and detecting lung nodules. In addition, we use novel state-of-the-art detection architectures, including, Faster-RCNN, YOLOv3, and SSD, for detection purposes. All deep learning models are evaluated using a publicly available benchmark LIDC-IDRI data set. The experimental outcomes reveal that the False Positive Rate (FPR) is reduced, and the accuracy is enhanced.
生物医学成像技术的最新进展为医疗保健行业和生物医学领域带来了巨大机遇。然而，对于医学专家来说，收集、测量和分析大量与健康相关的数据（如图像）是一项艰巨且耗时的工作。因此，在这方面，人工智能应用（包括机器学习和深度学习系统）有助于早期诊断各种传染性/癌症疾病，如肺癌。由于肺癌可能没有明显或清晰的初始症状，开发并推广一种能够在早期阶段辅助医学专家对肺结节进行分类和检测的计算机辅助检测（CAD）系统至关重要。因此，在本文中，我们通过对CT图像中的肺结节（即良性和恶性）进行分类和检测来分析肺癌诊断问题。为实现这一目标，引入了一种基于深度学习的自动化系统来对肺结节进行分类和检测。此外，我们使用了新颖的先进检测架构，包括Faster-RCNN、YOLOv3和SSD进行检测。所有深度学习模型均使用公开可用的基准LIDC-IDRI数据集进行评估。实验结果表明，误报率降低，准确率提高。
