Abstract
Pneumonia affects 7% of the global population, resulting in 2 million pediatric deaths every year. Chest X-ray (CXR) analysis is routinely performed to diagnose the disease. Computer-aided diagnostic (CADx) tools aim to supplement decision-making. These tools process the handcrafted and/or convolutional neural network (CNN) extracted image features for visual recognition. However, CNNs are perceived as black boxes since their performance lack explanations. This is a serious bottleneck in applications involving medical screening/diagnosis since poorly interpreted model behavior could adversely affect the clinical decision. In this study, we evaluate, visualize, and explain the performance of customized CNNs to detect pneumonia and further differentiate between bacterial and viral types in pediatric CXRs. We present a novel visualization strategy to localize the region of interest (ROI) that is considered relevant for model predictions across all the inputs that belong to an expected class. We statistically validate the models’ performance toward the underlying tasks. We observe that the customized VGG16 model achieves 96.2% and 93.6% accuracy in detecting the disease and distinguishing between bacterial and viral pneumonia respectively. The model outperforms the state-of-the-art in all performance metrics and demonstrates reduced bias and improved generalization.
Highlights
Pneumonia is a significant cause of mortality in children across the world
Rapid radiographic diagnoses and treatment are adversely impacted by the lack of expert radiologists in resource-constrained regions where pediatric pneumonia is highly endemic with alarming mortality rates
In this study, we evaluate, visualize, and explain the predictions of convolutional neural network (CNN) models in classifying we (i)pediatric detect and pneumonia in pediatric
Summary
Pneumonia is a significant cause of mortality in children across the world. Health Organization (WHO), around 2 million pneumonia-related deaths are reported every year in children under 5 years of age, making it the most significant cause of pediatric death [1]. Pneumonia sourced from bacterial and viral pathogens are the two leading causes and require different forms of management [2]. Bacterial pneumonia is immediately treated with antibiotics while viral pneumonia requires supportive care, making timely and accurate diagnosis important. Chest X-ray (CXR) analysis is the most commonly performed radiographic examination for diagnosing and differentiating the types of pneumonia [3]. Rapid radiographic diagnoses and treatment are adversely impacted by the lack of expert radiologists in resource-constrained regions where pediatric pneumonia is highly endemic with alarming mortality rates.
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