Multimodel ensemble-based Pneumonia x-ray image classification

Abstract

Pneumonia is a life-threatening respiratory infection that affects millions of individuals worldwide. Early and accurate diagnosis of pneumonia is crucial for effective treatment and patient care. In recent years, deep learning techniques have shown remarkable promise in automating the diagnosis of pneumonia from X-ray images. However, the inherent variability in X-ray images and the complexity of pneumonia patterns pose significant challenges to achieving high classification accuracy. In this paper, we propose a novel approach for pneumonia X-ray image classification based on multiple model ensemble. Our method leverages the strengths of diverse deep learning architectures and achieves superior classification performance compared to single models. We conducted extensive experiments on both public and private datasets, and the proposed method achieved accuracy improvements of 7.53 and 3.36, respectively. The experimental results indicate that the proposed method has high usability.