PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography

Pneumonia remains a leading cause of respiratory morbidity and mortality, underscoring the need for rapid and accurate diagnosis to enable timely treatment and prevent complications. This study introduces PELM (Pneumonia Ensemble Learning Model), a novel deep learning framework for automated pneumonia detection using chest X-ray (CXR) images. The model integrates four high-performing architectures—InceptionV3, VGG16, ResNet50, and Vision Transformer (ViT)—via feature-level concatenation to exploit complementary feature representations. A curated, large-scale dataset comprising 50,000 PA-view CXR images was assembled from NIH ChestX-ray14, CheXpert, PadChest, and Kaggle CXR Pneumonia datasets, including both pneumonia and non-pneumonia cases. To ensure fair benchmarking, all models were trained and evaluated under identical preprocessing and hyperparameter settings. PELM achieved outstanding performance, with 96% accuracy, 99% precision, 91% recall, 95% F1-score, 91% specificity, and an AUC of 0.91—surpassing individual model baselines and previously published methods. Additionally, comparative experiments were conducted using tabular clinical data from over 10,000 patients, enabling a direct evaluation of image-based and structured-data-based classification pipelines. These results demonstrate that ensemble learning with hybrid architectures significantly enhances diagnostic accuracy and generalization. The proposed approach is computationally efficient, clinically scalable, and particularly well-suited for deployment in low-resource healthcare settings, where radiologist access may be limited. PELM represents a promising advancement toward reliable, interpretable, and accessible AI-assisted pneumonia screening in global clinical practice.