Deep Learning in Multi-Class Lung Diseases' Classification on Chest X-ray Images.
Deep Learning in Multi-Class Lung Diseases' Classification on Chest X-ray Images.
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DOI：
10.3390/diagnostics12040915
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发表时间：
2022-04-06
期刊：
影响因子：
3.6
通讯作者：
Hong, Min
中科院分区：
文献类型：
--
作者：
Kim, Sungyeup;Rim, Beanbonyka;Choi, Seongjun;Lee, Ahyoung;Min, Sedong;Hong, Min
胸部x线影像（CXR）可以更早、更容易地诊断肺部疾病。因此，在本文中，我们提出了一种使用迁移学习技术对CXR图像上的肺部疾病进行分类的深度学习方法，以提高计算机辅助诊断系统（cad）诊断性能的效率和准确性。我们提出的方法是一步，端到端学习，这意味着原始CXR图像直接输入深度学习模型（EfficientNet v2-M），以提取其识别疾病类别的有意义的特征。我们在美国国立卫生研究院（NIH）的三类正常、肺炎和气胸数据集上进行了实验，获得了loss = 0.6933，准确率= 82.15%，灵敏度= 81.40%，特异性= 91.65%的验证性能。我们还对天安顺天香大学医院（SCH）的正常、肺炎、气胸和肺结核四类数据集进行了实验，验证性能为loss = 0.7658，准确率= 82.20%，灵敏度= 81.40%，特异性= 94.48%；正常、肺炎、气胸、肺结核的检测准确率分别为63.60%、82.30%、82.80%、89.90%。
Chest X-ray radiographic (CXR) imagery enables earlier and easier lung disease diagnosis. Therefore, in this paper, we propose a deep learning method using a transfer learning technique to classify lung diseases on CXR images to improve the efficiency and accuracy of computer-aided diagnostic systems’ (CADs’) diagnostic performance. Our proposed method is a one-step, end-to-end learning, which means that raw CXR images are directly inputted into a deep learning model (EfficientNet v2-M) to extract their meaningful features in identifying disease categories. We experimented using our proposed method on three classes of normal, pneumonia, and pneumothorax of the U.S. National Institutes of Health (NIH) data set, and achieved validation performances of loss = 0.6933, accuracy = 82.15%, sensitivity = 81.40%, and specificity = 91.65%. We also experimented on the Cheonan Soonchunhyang University Hospital (SCH) data set on four classes of normal, pneumonia, pneumothorax, and tuberculosis, and achieved validation performances of loss = 0.7658, accuracy = 82.20%, sensitivity = 81.40%, and specificity = 94.48%; testing accuracy of normal, pneumonia, pneumothorax, and tuberculosis classes was 63.60%, 82.30%, 82.80%, and 89.90%, respectively.
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