Detection of Pneumonia in Chest X-Ray Using Ensemble Learners and Transfer Learning with Deep Learning Models

Abstract

A commonly implemented imaging method used for detecting/diagnosing pneumonia is chest X-ray images, but interpreting these images can be extremely complicated requiring advanced skill sets. In this document, we introduce an innovative approach using ensemble learning which combines several pre-trained Deep Learning models' strengths with advanced transfer learning techniques aimed at enhancing lung infection detection through deep-learning predictive capacity utilization. We evaluated our methodology using publicly available datasets containing numerous X-ray scans resulting in optimally accurate predictions that could have substantial implications concerning medical practice improvement efficiency-wise when diagnosing patients exhibiting symptoms matching those indicative of pneumonic infections, thus allowing us to proceed forward successfully implementing more streamlined protocols going onward into future clinical settings.