BACKGROUND:Lung cancer is one of the most common diseases in the world. Computed tomography (CT) is the standard medical modality for early lung nodule detection and diagnosis that improves patient’s survival rate. Recently, deep learning algorithms, especially convolutional neural networks (CNNs),have become a preferred methodology for developing computer-aided detection and diagnosis (CAD) schemes of lung CT images. OBJECTIVE:Several CNN-based research projects have been initiated to design robust and efficient CAD schemes for the detection and classification of lung nodules. This paper reviews the recent works in this area and gives an insight into technical progress. METHODS:First, a brief overview of CNN models and their basic structures is presented in this investigation. Then, we provide an analytic comparison of the existing approaches to discover recent trend and upcoming challenges. We also introduce an objective description of both handcrafted and deep learning features, as well as the types of nodules, the medical imaging modalities, the widely used databases, and related works in the last three years. The articles presented in this work were selected from various databases. About 57% of reviewed articles published in the last year. RESULTS:Our analysis reveals that several methods achieved promising performance with high sensitivity rates ranging from 66% to 100% under the false-positive rates ranging from 1 to 15 per CT scan. It can be noted that CNN models have contributed to the accurate detection and early diagnosis of lung nodules. CONCLUSIONS:From the critical discussion and an outline for prospective directions, this survey provide researchers valuable information to master the deep learning concepts and to deepen their knowledge of the trend and latest techniques in developing CAD schemes of lung CT images.

中文翻译：

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基于深度学习的 CAD 方案，用于从 CT 图像中检测和分类肺结节：一项调查。

背景：肺癌是世界上最常见的疾病之一。计算机断层扫描 (CT) 是早期肺结节检测和诊断的标准医疗方式，可提高患者的生存率。最近，深度学习算法，尤其是卷积神经网络 (CNN)，已成为开发肺 CT 图像计算机辅助检测和诊断 (CAD) 方案的首选方法。目的：已经启动了几个基于 CNN 的研究项目，以设计用于肺结节检测和分类的稳健高效的 CAD 方案。本文回顾了该领域的最新工作，并对技术进展进行了深入了解。方法：首先，本研究简要概述了 CNN 模型及其基本结构。然后，我们提供了现有方法的分析比较，以发现最近的趋势和即将到来的挑战。我们还介绍了对手工制作和深度学习特征的客观描述，以及结节的类型、医学成像方式、广泛使用的数据库以及过去三年的相关工作。这项工作中介绍的文章选自各种数据库。去年发表的评论文章中约有 57%。结果：我们的分析表明，在每次 CT 扫描 1 到 15 的假阳性率范围内，几种方法取得了有希望的性能，其灵敏度范围为 66% 到 100%。可以注意到，CNN 模型为肺结节的准确检测和早期诊断做出了贡献。结论：