Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh.
J Healthc Eng. 2021 Feb 25;2021:8862089. doi: 10.1155/2021/8862089. eCollection 2021.
Pneumonia is a fatal disease responsible for almost one in five child deaths worldwide. Many developing countries have high mortality rates due to pneumonia because of the unavailability of proper and timely diagnostic measures. Using machine learning-based diagnosis methods can help to detect the disease early and in less time and cost. In this study, we proposed a novel method to determine the presence of pneumonia and identify its type (bacterial or viral) through analyzing chest radiographs. We performed a three-class classification based on features containing diverse information of the samples. After using an augmentation technique to balance the dataset's sample sizes, we extracted the chest X-ray images' statistical features, as well as global features by employing a deep learning architecture. We then combined both sets of features and performed the final classification using the RandomForest classifier. A feature selection method was also incorporated to identify the features with the highest relevance. We tested the proposed method on a widely used (but relabeled) chest radiograph dataset to evaluate its performance. The proposed model can classify the dataset's samples with an 86.30% classification accuracy and 86.03% F-score, which assert the model's efficacy and reliability. However, results show that the classifier struggles while distinguishing between viral and bacterial pneumonia samples. Implementing this method will provide a fast and automatic way to detect pneumonia in a patient and identify its type.
肺炎是一种致命疾病，在全球范围内导致近五分之一的儿童死亡。许多发展中国家由于缺乏适当和及时的诊断措施，肺炎死亡率很高。使用基于机器学习的诊断方法可以帮助早期、更快且更廉价地发现疾病。在本研究中，我们提出了一种通过分析胸部 X 光片来确定肺炎存在和识别其类型（细菌或病毒）的新方法。我们基于包含样本多种信息的特征进行了三分类。在使用扩充技术平衡数据集的样本大小后，我们提取了胸部 X 光图像的统计特征和全局特征，采用深度学习架构。然后，我们结合了这两组特征，并使用随机森林分类器进行最终分类。还采用了特征选择方法来识别具有最高相关性的特征。我们在一个广泛使用（但重新标记）的胸部 X 光片数据集上测试了所提出的方法，以评估其性能。所提出的模型可以对数据集的样本进行分类，分类准确率为 86.30%，F1 得分为 86.03%，这表明了模型的有效性和可靠性。然而，结果表明，该分类器在区分病毒和细菌肺炎样本方面存在困难。实施这种方法将为患者提供一种快速自动的肺炎检测和类型识别方法。
