Department, of Computer Engineering, Near East University, North Cyprus, Mersin-10, Turkey.
J Healthc Eng. 2018 Aug 1;2018:4168538. doi: 10.1155/2018/4168538. eCollection 2018.
Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.
胸部疾病是人们生活中非常严重的健康问题。这些疾病包括慢性阻塞性肺疾病、肺炎、哮喘、肺结核和肺部疾病。胸部疾病的及时诊断非常重要。为此已经开发了许多方法。在本文中，我们展示了使用传统和深度学习方法对胸部 X 光片中的胸部病理进行分类的可行性。本文提出了卷积神经网络 (CNN) 用于诊断胸部疾病。介绍了 CNN 的体系结构及其设计原理。为了进行比较，还构建了具有监督学习的反向传播神经网络 (BPNN) 和具有无监督学习的竞争神经网络 (CpNN) 用于诊断胸部疾病。所考虑的所有网络 CNN、BPNN 和 CpNN 都在相同的胸部 X 射线数据库上进行训练和测试，并讨论了每个网络的性能。给出了网络之间在准确性、错误率和训练时间方面的比较结果。
