Computer Science and Engineering Department, Supreme Knowledge Foundation Group of Institutions, Hooghly, 712139, India.
Electrical Engineering Department, Jadavpur University, Kolkata, 700032, India.
J Digit Imaging. 2020 Jun;33(3):655-677. doi: 10.1007/s10278-020-00320-6.
This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is subjective and depends on the radiologist's experiences. Owing to the variation in shapes and appearances of a lung nodule, it is very difficult to identify the proper location of the nodule from a huge number of slices generated by the CT scanner. Small nodules (< 10 mm in diameter) may be missed by this manual detection process. Therefore, computer-aided diagnosis (CAD) system acts as a "second opinion" for the radiologists, by making final decision quickly with higher accuracy and greater confidence. The goal of this survey work is to present the current state of the artworks and their progress towards lung nodule detection to the researchers and readers in this domain. This review paper has covered the published works from 2009 to April 2018. Different nodule detection approaches are described elaborately in this work. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks.
这篇论文对文献进行了系统的回顾，重点是胸部计算机断层扫描（CT）图像中的肺结节检测。放射科医生手动检测肺结节是一个顺序和耗时的过程。这种检测是主观的，取决于放射科医生的经验。由于肺结节的形状和外观各异，从 CT 扫描仪生成的大量切片中很难确定结节的正确位置。这种手动检测过程可能会遗漏小的结节（直径<10 毫米）。因此，计算机辅助诊断（CAD）系统可以作为放射科医生的“第二意见”，通过快速做出最终决策，提高准确性和可信度。这项调查工作的目的是向该领域的研究人员和读者展示当前的研究成果及其在肺结节检测方面的进展。这篇综述涵盖了 2009 年至 2018 年 4 月发表的研究工作。这项工作详细描述了不同的结节检测方法。最近，人们观察到基于深度学习（DL）的方法被广泛应用于结节检测和特征提取。因此，本文重点介绍了基于卷积神经网络（CNN）的深度学习方法，描述了不同的基于 CNN 的网络。
