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A machine learning approach on chest X-rays for pediatric pneumonia detection
Barakat, Natali Imad Awad, Mahmoud Abu-Nabah, Bassam
Barakat, Natali Imad
Awad, Mahmoud
Abu-Nabah, Bassam
Date
2023-06-04
Advisor
Type
Article
Peer-Reviewed
Published version
Peer-Reviewed
Published version
Degree
Description
Abstract
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.