Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks

Abstract

Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks
 ABSTRACT
 Studies on medical imaging have grown significantly in recent years. Doctors have a crucial convenience for diagnosis thanks to semi- or fully automatic region recognition in medical imaging. It is crucial to support treatment without a specialist doctor, particularly in those nations where there is a dearth of such medical professionals. The little air sacs known as alveoli are most impacted by pneumonia, a lung inflammation. A key component of providing the right therapy conditions to heal patients and reduce harm while eradicating inflammation is early detection and precise diagnosis. Noise and blurring in patient photos obtained from X-ray machines are cleaned using deep learning algorithms and image processing techniques, and they are very helpful in. In this study, we studied chest X-ray images of pediatric patients with pneumonia and healthy individuals. XGBoost (eXtreme gradient boosting) is an innovative machine learning algorithm based on decision tree and using gradient boosting in its computations. It achieved 97.01% success with high classification performance.
 Keywords: Medical imaging, Machine learning, Pediatric Chest X-ray