IEEE/ACM Trans Comput Biol Bioinform. 2021 May-Jun;18(3):951-962. doi: 10.1109/TCBB.2019.2911947. Epub 2021 Jun 3.
Pneumothorax is a common pulmonary disease that can lead to dyspnea and can be life-threatening. X-ray examination is the main means to diagnose this disease. Computer-aided diagnosis of pneumothorax on chest X-ray, as a prerequisite for a timely cure, has been widely studied, but it is still not satisfactory to achieve highly accurate results. In this paper, an image classification algorithm based on the deep convolutional neural network (DCNN) is proposed for high-resolution medical image analysis of pneumothorax X-rays, which features a Network In Network (NIN) for cleaning the data, random histogram equalization data augmentation processing, and a DCNN. The experimental results indicate that the proposed method can effectively increase the correct diagnosis rate of pneumothorax, and the Area under Curve (AUC) of the test verified in the experiment is 0.9844 on ZJU-2 test data and 0.9906 on the ChestX-ray14, respectively. In addition, a large number of atmospheric pleura samples are visualized and analyzed based on the experimental results and in-depth learning characteristics of the algorithm. The analysis results verify the validity of feature extraction for the network. Combined with the results of these two aspects, the proposed X-ray image processing algorithm can effectively improve the classification accuracy of pneumothorax photographs.
气胸是一种常见的肺部疾病，可导致呼吸困难，甚至危及生命。X 射线检查是诊断这种疾病的主要手段。计算机辅助诊断气胸的 X 射线检查作为及时治疗的前提条件，已经得到了广泛的研究，但要达到高度准确的结果仍不尽如人意。本文提出了一种基于深度卷积神经网络（DCNN）的图像分类算法，用于高分辨率气胸 X 射线医学图像分析，该算法具有用于数据清洗的网络内网络（NIN）、随机直方图均衡化数据增强处理和 DCNN。实验结果表明，该方法可以有效提高气胸的正确诊断率，在 ZJU-2 测试数据上的实验验证的曲线下面积（AUC）分别为 0.9844，在 ChestX-ray14 上为 0.9906。此外，还基于实验结果和算法的深度学习特征对大量大气胸膜样本进行了可视化和分析。分析结果验证了网络的特征提取有效性。结合这两方面的结果，提出的 X 射线图像处理算法可以有效提高气胸照片的分类准确率。
