Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, India.
Cancer Metastasis Rev. 2020 Sep;39(3):989-998. doi: 10.1007/s10555-020-09901-x.
Lung cancer is one of the most common diseases among humans and one of the major causes of growing mortality. Medical experts believe that diagnosing lung cancer in the early phase can reduce death with the illustration of lung nodule through computed tomography (CT) screening. Examining the vast amount of CT images can reduce the risk. However, the CT scan images incorporate a tremendous amount of information about nodules, and with an increasing number of images make their accurate assessment very challenging tasks for radiologists. Recently, various methods are evolved based on handcraft and learned approach to assist radiologists. In this paper, we reviewed different promising approaches developed in the computer-aided diagnosis (CAD) system to detect and classify the nodule through the analysis of CT images to provide radiologists' assistance and present the comprehensive analysis of different methods.
肺癌是人类最常见的疾病之一，也是死亡率不断上升的主要原因之一。医学专家认为，通过计算机断层扫描 (CT) 筛查早期诊断肺癌可以降低死亡率。通过对大量 CT 图像进行检查，可以降低风险。然而，CT 扫描图像包含大量有关结节的信息，并且随着图像数量的增加，对其进行准确评估对放射科医生来说是一项极具挑战性的任务。最近，已经基于手工和学习方法开发了各种方法来协助放射科医生。在本文中，我们回顾了计算机辅助诊断 (CAD) 系统中通过分析 CT 图像来检测和分类结节的不同有前途的方法，为放射科医生提供协助，并对不同方法进行了全面分析。
