Detection and Classification of Pulmonary Nodules Using Convolutional Neural Networks: A Survey

Abstract

CT screening has been proven to be effective for diagnosing lung cancer at its early manifestation in the form of pulmonary nodules, thus decreasing the mortality. However, the exponential increase of image data makes their accurate assessment a very challenging task given that the number of radiologists is limited and they have been overworked. Recently, numerous methods, especially ones based on deep learning with convolutional neural network (CNN), have been developed to automatically detect and classify pulmonary nodules in medical images. In this paper, we present a comprehensive analysis of these methods and their performances. First, we briefly introduce the fundamental knowledge of CNN as well as the reasons for their suitability to medical images analysis. Then, a brief description of various medical images datasets, as well as the environmental setup essential for facilitating lung nodule investigations with CNNs, is presented. Furthermore, comprehensive overviews of recent progress in pulmonary nodule analysis using CNNs are provided. Finally, existing challenges and promising directions for further improving the application of CNN to medical images analysis and pulmonary nodule assessment, in particular, are discussed. It is shown that CNNs have transformed greatly the early diagnosis and management of lung cancer. We believe that this review will provide all the medical research communities with the necessary knowledge to master the concept of CNN so as to utilize it for improving the overall human healthcare system.

Keywords

• Lung cancer
• deep learning
• convolutional neural networks
• computed tomography (CT) images
• pulmonary nodules
• image classification