Prakash J Arun, Ravi Vinayakumar, Sowmya V, Soman K P
Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.
Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.
Neural Comput Appl. 2023;35(11):8259-8279. doi: 10.1007/s00521-022-08099-z. Epub 2022 Dec 7.
Pneumonia is an acute respiratory infection caused by bacteria, viruses, or fungi and has become very common in children ranging from 1 to 5 years of age. Common symptoms of pneumonia include difficulty breathing due to inflamed or pus and fluid-filled alveoli. The United Nations Children's Fund reports nearly 800,000 deaths in children due to pneumonia. Delayed diagnosis and overpriced tests are the prime reason for the high mortality rate, especially in underdeveloped countries. A time and cost-efficient diagnosis tool: Chest X-rays, was thus accepted as the standard diagnostic test for pediatric pneumonia. However, the lower radiation levels for diagnosis in children make the task much more onerous and time-consuming. The mentioned challenges initiate the need for a computer-aided detection model that is instantaneous and accurate. Our work proposes a stacked ensemble learning of deep learning-based features for pediatric pneumonia classification. The extracted features from the global average pooling layer of the fine-tuned Xception model pretrained on ImageNet weights are sent to the Kernel Principal Component Analysis for dimensionality reduction. The dimensionally reduced features are further trained and validated on the stacking classifier. The stacking classifier consists of two stages; the first stage uses the Random-Forest classifier, K-Nearest Neighbors, Logistic Regression, XGB classifier, Support Vector Classifier (SVC), Nu-SVC, and MLP classifier. The second stage operates on Logistic Regression using the first stage predictions for the final classification with Stratified K-fold cross-validation to prevent overfitting. The model was tested on the publicly available pediatric pneumonia dataset, achieving an accuracy of 98.3%, precision of 99.29%, recall of 98.36%, F1-score of 98.83%, and an AUC score of 98.24%. The performance shows its reliability for real-time deployment in assisting radiologists and physicians.
肺炎是由细菌、病毒或真菌引起的急性呼吸道感染，在1至5岁的儿童中非常常见。肺炎的常见症状包括因肺泡发炎或充满脓液和液体而导致呼吸困难。联合国儿童基金会报告称，近80万儿童死于肺炎。诊断延迟和检测费用过高是死亡率居高不下的主要原因，尤其是在欠发达国家。因此，一种省时且经济高效的诊断工具——胸部X光，被公认为小儿肺炎的标准诊断测试。然而，儿童诊断所需的较低辐射水平使得这项任务更加艰巨和耗时。上述挑战引发了对一种即时且准确的计算机辅助检测模型的需求。我们的工作提出了一种基于深度学习特征的堆叠集成学习方法，用于小儿肺炎分类。从在ImageNet权重上预训练的微调Xception模型的全局平均池化层提取的特征被发送到核主成分分析进行降维。降维后的特征在堆叠分类器上进一步训练和验证。堆叠分类器由两个阶段组成；第一阶段使用随机森林分类器、K近邻、逻辑回归、XGB分类器、支持向量分类器（SVC）、Nu-SVC和多层感知器分类器。第二阶段使用第一阶段的预测结果，通过分层K折交叉验证在逻辑回归上进行最终分类，以防止过拟合。该模型在公开可用的小儿肺炎数据集上进行了测试，准确率达到98.3%，精确率为99.29%，召回率为98.36%，F1分数为98.83%，AUC分数为98.24%。该性能表明其在协助放射科医生和医生进行实时部署方面的可靠性。
