A machine learning approach on chest X-rays for pediatric pneumonia detection

According to the World Health Organization (WHO), pneumonia is the leading infectious cause of death in children below 5 years old. Hence, the early detection of pediatric pneumonia is crucial to reduce its morbidity and mortality rates. Even though chest radiography is the most commonly employed modality for pneumonia detection, recent studies highlight the existence of poor interobserver agreement in the chest X-ray interpretation of healthcare practitioners when it comes to diagnosing pediatric pneumonia. Thus, there is a significant need for automating the detection process to minimize the potential human error. Since Artificial Intelligence tools such as Deep Learning (DL) and Machine Learning (ML) have the potential to automate disease detection, many researchers explored how such tools can be implemented to detect pneumonia in chest X-rays. Notably, the majority of efforts tackled this problem from a DL point of view. However, ML has shown a higher potential for medical interpretability while being less computationally demanding than DL.

The aim of this paper is to automate the early detection process of pediatric pneumonia using ML as it is less computationally demanding than DL.

The proposed approach entails performing data augmentation to balance the classes of the utilized dataset, optimizing the feature extraction scheme, and evaluating the performance of several ML models. Moreover, the performance of this approach is compared to a TL benchmark to evaluate its candidacy.

Using the proposed approach, the Quadratic SVM model yielded an accuracy of 97.58%, surpassing the accuracies reported in the current ML literature. In addition, this model classification time was significantly smaller than that of the TL benchmark.

The results strongly support the candidacy of the proposed approach in reliably detecting pediatric pneumonia.