Detecting pneumonia in chest radiographs using convolutional neural networks

• Main Authors: Ureta, Jennifer C., Aran, Oya, Rivera, Joanna Pauline
• Format: text
• Published: Animo Repository 2020
• Subjects: Chest—Radiography; Pneumonia—Imaging; Image processing; Computer Sciences; Software Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2623
• Institution: De La Salle University

Pneumonia is an infection of the lungs that can cause mild to severe illness and affects millions of people worldwide. Imaging studies are therefore crucial for the detection and management of patients with pneumonia, and radiography is currently the best method for diagnosis. However, clinical diagnosis of chest X-rays can be a challenging task as it requires interpretation by highly trained clinicians. This study uses deep learning to perform binary classification of frontal-view chest X-ray images to detect signs of childhood pneumonia. The effectiveness of the classifiers was validated using a dataset that was collected by [5] containing 5,856 labeled X-ray images from children. The classifiers were able to identify the presence or absence of childhood pneumonia with an accuracy between 96-97%. © 2020 SPIE.