Computer Assisted Detection and Diagnosis of Pediatric Pneumonia in Chest X-ray Images

Diagnostic Imaging

Computer Assisted Detection and Diagnosis of Pediatric Pneumonia in Chest X-ray Images

Machine learning approaches, such as deep learning, have exceeded human performance across many visual tasks. They’ve done so by using automated hierarchical feature extraction and classification by multiple layers to detect distinct patterns in high-dimensional data sets allowing quantification of the disease state and the prediction of the course the disease will take. In this white paper, we present a deep learning-based algorithm that can accurately detect community-acquired pediatric pneumonia and distinguish its etiology in chest X-ray images. Notably, our model outperformed state-of-the-art classifiers in terms of all performance metrics across the classification tasks. Furthermore, our algorithm can localize areas that are most indicative of infection without using any localization data to train the system.

Read the paper here.