Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, China; Dept. of Computer Science, COMSATS Institute of Information Technology, Pakistan.
Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, China.
J Biomed Inform. 2018 Mar;79:117-128. doi: 10.1016/j.jbi.2018.01.005. Epub 2018 Jan 31.
Pulmonary cancer is considered as one of the major causes of death worldwide. For the detection of lung cancer, computer-assisted diagnosis (CADx) systems have been designed. Internet-of-Things (IoT) has enabled ubiquitous internet access to biomedical datasets and techniques; in result, the progress in CADx is significant. Unlike the conventional CADx, deep learning techniques have the basic advantage of an automatic exploitation feature as they have the ability to learn mid and high level image representations. We proposed a Computer-Assisted Decision Support System in Pulmonary Cancer by using the novel deep learning based model and metastasis information obtained from MBAN (Medical Body Area Network). The proposed model, DFCNet, is based on the deep fully convolutional neural network (FCNN) which is used for classification of each detected pulmonary nodule into four lung cancer stages. The performance of proposed work is evaluated on different datasets with varying scan conditions. Comparison of proposed classifier is done with the existing CNN techniques. Overall accuracy of CNN and DFCNet was 77.6% and 84.58%, respectively. Experimental results illustrate the effectiveness of proposed method for the detection and classification of lung cancer nodules. These results demonstrate the potential for the proposed technique in helping the radiologists in improving nodule detection accuracy with efficiency.
肺癌被认为是全球主要死因之一。为了检测肺癌，已经设计了计算机辅助诊断 (CADx) 系统。物联网 (IoT) 实现了对生物医学数据集和技术的无处不在的互联网访问；因此，CADx 的进展非常显著。与传统的 CADx 不同，深度学习技术具有自动利用特征的基本优势，因为它们能够学习中高级别的图像表示。我们通过使用基于新型深度学习的模型和从 MBAN（医疗体域网）获得的转移信息，提出了一种用于肺癌的计算机辅助决策支持系统。所提出的模型 DFCNet 基于深度全卷积神经网络 (FCNN)，用于将每个检测到的肺结节分类为四个肺癌阶段。在所提出的工作中，使用不同的扫描条件评估不同数据集的性能。将所提出的分类器与现有的 CNN 技术进行比较。CNN 和 DFCNet 的总体准确率分别为 77.6%和 84.58%。实验结果说明了所提出的方法用于检测和分类肺癌结节的有效性。这些结果表明，所提出的技术有可能帮助放射科医生提高结节检测的准确性和效率。
