Pneumonia is one of the most common and fatal diseases in the world. Early diagnosis and treatment are important factors in reducing mortality caused by the aforementioned disease. One of the most important and common techniques to diagnose pneumonia disease is the X‐ray images. By evaluating these images, various machine‐learning methods are used for accuracy in diagnosis. The presented study in this article utilizes machine‐learning techniques to evaluate these X‐ray images. The diagnosis of pediatric pneumonia is classified with a proposed machine learning method by using the chest X‐ray images. The proposed system firstly utilizes a two‐dimensional discrete wavelet transform to extract features from images. The features obtained from the wavelet method are labeled as normal and pneumonia and applied to the classifier for classification. Besides, Random Forest algorithm is used for the classification technique of 5856 X‐ray images. A 10‐fold cross‐validation method is used to evaluate the success of the proposed method and to ensure that the system avoided overfitting. By using various machine learning algorithms, simulation results reveal that the Random Forest method is proposed and it gives successful results. Results also show that, at the end of the training and validation process, the proposed method achieves higher success with an accuracy of 97.11%.

中文翻译：

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利用随机森林的二维离散小波特征提取检测胸部X射线图像中的肺炎

肺炎是世界上最常见和致命的疾病之一。早期诊断和治疗是降低上述疾病引起的死亡率的重要因素。X射线图像是诊断肺炎最重要，最常见的技术之一。通过评估这些图像，可以使用各种机器学习方法来提高诊断的准确性。本文中提出的研究利用机器学习技术来评估这些X射线图像。小儿肺炎的诊断方法是使用胸部X射线图像，通过一种建议的机器学习方法进行分类。提出的系统首先利用二维离散小波变换从图像中提取特征。从小波方法获得的特征被标记为正常和肺炎，并应用于分类器进行分类。此外，随机森林算法用于5856个X射线图像的分类技术。10倍交叉验证方法用于评估所提出方法的成功性，并确保系统避免过拟合。通过使用各种机器学习算法，仿真结果表明，提出了随机森林方法，并获得了成功的结果。结果还表明，在训练和验证过程的最后，该方法以97.11％的准确性获得了更高的成功率。仿真结果表明，提出了随机森林法，并取得了成功的结果。结果还表明，在训练和验证过程的最后，该方法以97.11％的准确性获得了更高的成功率。仿真结果表明，提出了随机森林法，并取得了成功的结果。结果还表明，在训练和验证过程的最后，该方法以97.11％的准确性获得了更高的成功率。