We investigated whether a convolutional neural network (CNN) can enhance the usability of computer‐aided detection (CAD) of chest radiographs for various pulmonary abnormal lesions. The numbers of normal and abnormal patients were 6055 and 3463, respectively. Two radiologists delineated regions of interest for lesions and labeled the disease types as ground truths. The datasets were split into training, tuning, and testing as 7:1: 2. Total test sets were randomly selected in 1214 normal and 690 abnormal. A 5‐fold, cross‐validation was performed on our datasets. For the classification of normal and abnormal, we developed a CNN based on DenseNet169; for abnormal detection, The You Only Look Once (YOLO) v2 with DenseNet was used. Detection and classification of normal and five classes of diseases (nodule[s], consolidation, interstitial opacity, pleural effusion, and pneumothorax) on chest radiographs were analyzed. Our CNN model classified chest radiographs as normal or abnormal with an accuracy of 97.8%. For the results of the abnormal, F1 score, was 75.2 ± 2.28% for nodules, 55.0 ± 4.3% for consolidation, 78.2 ± 7.85% for interstitial opacity, 81.6 ± 2.07% for pleural effusion, and 70.0 ± 7.97% for pneumothorax, respectively. In addition, we conducted the experiments between our method and RetinaNet with only nodules. The results of our method and RetinaNet at cutoff‐0.5 in the free response operating characteristic curve were 83.45% and 80.55%, respectively. Our algorithm demonstrated viable detection and disease classification capacity and could be used for CAD of lung diseases on chest radiographs.

中文翻译：

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深胸部X射线：基于深度卷积神经网络的病变检测和分类

我们研究了卷积神经网络（CNN）是否可以增强胸部X线片对各种肺部异常病变的计算机辅助检测（CAD）的可用性。正常和异常患者分别为6055和3463。两名放射科医生划定了病变区域，并将疾病类型标记为事实。数据集按7：1：2的比例分为训练，调整和测试。2.在1214正常和690异常中随机选择了总测试集。对我们的数据集进行了5倍交叉验证。对于正常和异常的分类，我们开发了基于DenseNet169的CNN。为了进行异常检测，使用了带有DenseNet的“您只看一次（YOLO）v2”。正常疾病和五类疾病（结节，合并，间质混浊，胸片上的胸腔积液和气胸）进行了分析。我们的CNN模型将胸部X光片分为正常或异常，准确率为97.8％。对于异常结果，结节的F1评分为75.2±2.28％，结节为55.0±4.3％，间质浑浊为78.2±7.85％，胸腔积液为81.6±2.07％，气胸为70.0±7.97％ 。此外，我们仅在结核与RetinaNet之间进行了实验。我们的方法和RetinaNet在自由响应操作特性曲线中的截止值为0.5时的结果分别为83.45％和80.55％。我们的算法证明了可行的检测能力和疾病分类能力，可用于胸部X光片上的肺部疾病CAD。我们的CNN模型将胸部X光片分为正常或异常，准确率为97.8％。对于异常结果，结节的F1评分为75.2±2.28％，结节为55.0±4.3％，间质浑浊为78.2±7.85％，胸腔积液为81.6±2.07％，气胸为70.0±7.97％ 。此外，我们仅在结核与RetinaNet之间进行了实验。我们的方法和RetinaNet在自由响应操作特性曲线中的截止值为0.5时的结果分别为83.45％和80.55％。我们的算法证明了可行的检测能力和疾病分类能力，可用于胸部X光片上的肺部疾病CAD。我们的CNN模型将胸部X光片分为正常或异常，准确率为97.8％。对于异常结果，结节的F1评分为75.2±2.28％，结节为55.0±4.3％，间质浑浊为78.2±7.85％，胸腔积液为81.6±2.07％，气胸为70.0±7.97％ 。此外，我们仅在结节的情况下进行了我们的方法与RetinaNet之间的实验。我们的方法和RetinaNet在自由响应操作特性曲线中的截止值为0.5时的结果分别为83.45％和80.55％。我们的算法证明了可行的检测和疾病分类能力，可用于胸部X光片上的肺部疾病CAD。巩固3％，间质不透明78.2±7.85％，胸腔积液81.6±2.07％，气胸70.0±7.97％。此外，我们仅在结核与RetinaNet之间进行了实验。我们的方法和RetinaNet在自由响应操作特性曲线中的截止值为0.5时的结果分别为83.45％和80.55％。我们的算法证明了可行的检测能力和疾病分类能力，可用于胸部X光片上的肺部疾病CAD。巩固3％，间质不透明78.2±7.85％，胸腔积液81.6±2.07％，气胸70.0±7.97％。此外，我们仅在结核与RetinaNet之间进行了实验。我们的方法和RetinaNet在自由响应操作特性曲线中的截止值为0.5时的结果分别为83.45％和80.55％。我们的算法证明了可行的检测和疾病分类能力，可用于胸部X光片上的肺部疾病CAD。自由响应操作特性曲线中的5分别为83.45％和80.55％。我们的算法证明了可行的检测和疾病分类能力，可用于胸部X光片上的肺部疾病CAD。自由响应操作特性曲线中的5分别为83.45％和80.55％。我们的算法证明了可行的检测和疾病分类能力，可用于胸部X光片上的肺部疾病CAD。