The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.

中文翻译：

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用于移动设备的CT扫描图像中的肺结节检测的浅层模型

认知计算和大数据分析的出现彻底改变了医疗保健领域，尤其是在检测癌症方面。肺癌是全世界死亡的主要原因之一。肺部的肺结节在发育后可能会癌变。早期发现肺结节可导致早期治疗并显着减少死亡。在本文中，我们提出了一种基于端到端的卷积神经网络（CNN）的自动肺结节检测和分类系统。提出的CNN架构只有四个卷积层，因此本质上是轻巧的。每个卷积层包括两个连续的卷积块，一个连接器卷积块，每个块之后的非线性激活函数以及一个合并块。实验是使用肺图像数据库协会（LIDC）数据库进行的。从LIDC数据库中，选择了1279个样本图像，其中569个是非癌性的，278个是良性的，其余是恶性的。拟议的系统达到了97.9％的精度。与其他著名的CNN架构相比，所提出的架构具有更少的触发器和参数，因此适用于实时医学图像分析。