Early detection of pneumonia disease can increase the survival rate of lung patients. Chest X-ray (CXR) images are the primarily means of detecting and diagnosing pneumonia. Detecting pneumonia from CXR images by a trained radiologist is a challenging task. It needs an automatic computer-aided diagnostic system to improve the accuracy of diagnosis. Developing a lightweight automatic pneumonia detection approach for energy-efficient medical systems plays an important role in improving the quality of healthcare with reduced costs and speedier response. Recent works have proposed to develop automated detection models using deep learning (DL) methods. However, the efficiency and effectiveness of these models need to be improved because they depend on the values of the models’ hyperparameters. Choosing suitable hyperparameter values is a critical task for constructing a lightweight and accurate model. In this paper, a lightweight DL approach is proposed using a pretrained DenseNet-121-based feature extraction method and a deep neural network- (DNN-) based method with a random search fine-tuning technique. The DenseNet-121 model is selected due to its ability to provide the best representation of lung features. The use of random search makes the tuning process faster and improves the efficiency and accuracy of the DNN model. An extensive set of experiments are conducted on a public dataset of CXR images using a set of evaluation metrics. The experiments show that the approach achieved 98.90% accuracy with an increase of 0.47% compared to the latest approach on the same dataset. Moreover, the experimental results demonstrate the approach that the average execution time for detection is very low, confirming its suitability for energy-efficient medical systems.

中文翻译：

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基于轻量级深度学习的节能型肺炎肺炎检测方法

肺炎疾病的早期发现可以提高肺部患者的生存率。胸部X射线（CXR）图像是检测和诊断肺炎的主要手段。由训练有素的放射科医生从CXR图像中检测出肺炎是一项艰巨的任务。它需要一个自动的计算机辅助诊断系统来提高诊断的准确性。开发用于节能医疗系统的轻型自动肺炎检测方法，在以降低的成本和更快的响应速度提高医疗质量方面起着重要作用。最近的工作已提出使用深度学习（DL）方法来开发自动检测模型。但是，这些模型的效率和有效性需要提高，因为它们取决于模型的超参数的值。选择合适的超参数值是构建轻量级且准确模型的关键任务。在本文中，提出了一种轻量级的DL方法，该方法使用了基于预训练的DenseNet-121的特征提取方法和基于深度神经网络（DNN-）的方法，并采用了随机搜索微调技术。选择DenseNet-121模型是因为它能够提供最佳的肺部特征。随机搜索的使用使调整过程更快，并提高了DNN模型的效率和准确性。使用一组评估指标对CXR图像的公共数据集进行了广泛的实验。实验表明，与同一数据集上的最新方法相比，该方法达到了98.90％的准确性，增加了0.47％。而且，