Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
Medical Image and Signal Processing Research Core, Iran University of Medical Sciences, Tehran, Iran.
Br J Radiol. 2021 May 1;94(1121):20201263. doi: 10.1259/bjr.20201263. Epub 2021 Apr 16.
Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs.
Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models ( VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1-5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score.
All proposed models provide accuracy greater than 83.0% for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8% accuracy, followed by Xception model with an accuracy of 86.0%. The sensitivity of the proposed models was greater than 91.0%. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0%. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively.
Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019.
Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0% for binary classification.
肺炎是一种肺部感染，会导致一个或两个肺部的小气囊（肺泡）发炎。为了实现最佳的患者护理，早期对肺炎进行适当和快速的诊断至关重要。目前，胸部 X 射线被认为是诊断肺炎的最佳成像方式。然而，胸部 X 射线图像的解释具有挑战性。为此，我们旨在使用基于自动化卷积神经网络的迁移学习方法来检测儿科胸部 X 光片中的肺炎。
在此，我们应用了一种基于自动化卷积神经网络的迁移学习方法，使用了四种不同的预训练模型（VGG19、DenseNet121、Xception 和 ResNet50）来检测儿童（1-5 岁）胸部 X 光片中的肺炎。使用五种性能指标评估不同提出模型对测试数据集的性能，包括准确性、敏感性/召回率、精度、曲线下面积和 F1 分数。
所有提出的模型对于二分类的准确率均大于 83.0%。预训练的 DenseNet121 模型提供了最高的自动化肺炎分类性能，准确率为 86.8%，其次是 Xception 模型，准确率为 86.0%。所提出模型的敏感性均大于 91.0%。Xception 和 DenseNet121 模型的 F1 分数大于 89.0%，达到了最高的分类性能。VGG19、Xception、ResNet50 和 DenseNet121 模型的接收器工作特征曲线下面积分别为 0.78、0.81、0.81 和 0.86。
我们的数据表明，所提出的模型对于二分类具有很高的准确性。迁移学习被用于加速所提出模型的训练，并解决与数据不足相关的问题。我们希望这些提出的模型可以帮助放射科医生在放射科快速诊断肺炎。此外，我们提出的模型可能有助于检测其他与胸部相关的疾病，如新型冠状病毒 2019。
在此，我们使用迁移学习作为机器学习方法来加速所提出模型的训练，并解决与数据不足相关的问题。我们提出的模型对于二分类的准确率大于 83.0%。
