Sriporn Krit, Tsai Cheng-Fa, Tsai Chia-En, Wang Paohsi
Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan.
Department of Management Information Systems, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan.
Healthcare (Basel). 2020 Apr 23;8(2):107. doi: 10.3390/healthcare8020107.
Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.
图像处理技术和计算机辅助诊断是用于支持为肺病患者提供治疗的放射科医生和医学专业人员决策过程的医疗技术。这些方法涉及使用胸部X光图像来诊断和检测肺部病变，但有时会有一些异常情况需要一段时间才会出现。本实验使用5810张图像对MobileNet、Densenet - 121和Resnet - 50模型进行训练和验证，这些都是用于图像分类准确性的常用网络，并采用旋转技术调整肺部疾病数据集，以支持使用这些卷积神经网络模型进行学习。卷积神经网络模型评估结果表明，具有先进的Mish激活函数和Nadam优化性能的Densenet - 121，其准确率、召回率、精确率和F1值的总和为98.88%。然后，我们使用该模型对非数据集训练和验证中10%的总图像进行测试。结果的准确率为98.97%，为开发计算机辅助诊断系统提供了重要组成部分，以实现肺部病变检测的最佳性能。
