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.

中文翻译：

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胸部 CT 图像中从特征工程到深度学习的肺结节检测：综合回顾。

本文对侧重于胸部计算机断层扫描 (CT) 图像中肺结节检测的文献进行了系统回顾。放射科医师手动检测肺结节是一个连续且耗时的过程。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的变化，很难从 CT 扫描仪生成的大量切片中识别出肺结节的正确位置。此手动检测过程可能会遗漏小结节（直径 < 10 毫米）。因此，计算机辅助诊断 (CAD) 系统充当放射科医生的“第二意见”，以更高的准确性和更大的信心快速做出最终决定。这项调查工作的目标是向该领域的研究人员和读者展示艺术品的现状及其在肺结节检测方面的进展。这篇综述论文涵盖了 2009 年至 2018 年 4 月发表的作品。本文详细描述了不同的结节检测方法。最近，据观察，基于深度学习 (DL) 的方法被广泛应用于结节检测和表征。因此，通过描述不同的基于 CNN 的网络，重点介绍了基于卷积神经网络 (CNN) 的 DL 方法。据观察，基于深度学习 (DL) 的方法被广泛应用于结节检测和表征。因此，通过描述不同的基于 CNN 的网络，重点介绍了基于卷积神经网络 (CNN) 的 DL 方法。据观察，基于深度学习 (DL) 的方法被广泛应用于结节检测和表征。因此，通过描述不同的基于 CNN 的网络，重点介绍了基于卷积神经网络 (CNN) 的 DL 方法。