# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 8 Innovative Contribution  
This study presents several key innovations in the field of AI-assisted medical diagnostics, particularly in the automated detection of pneumonia from chest X-ray images. Foremost among these is the introduction of PELM, a novel ensemble model that integrates the complementary strengths of four state-of-the-art deep learning architectures--InceptionV3, VGG16, ResNet50, and Vision Transformer. By leveraging the distinct representational capabilities of both convolutional and transformer-based models, PELM achieves outstanding performance across all major evaluation metrics, including 96% accuracy, 99% precision, 91% recall, 95% F1-score, 91% specificity, and an AUC of 0.91. A major differentiator of this work is its comprehensive evaluation on a large and diverse dataset comprising 44,836 PA-view CXR images--far exceeding the scale of most prior studies. This significantly improves the model's generalizability and enhances its clinical relevance. The study also introduces an optimized preprocessing pipeline that incorporates noise reduction and contrast enhancement using CLAHE, facilitating superior feature extraction and improving robustness under varied image quality conditions. PELM consistently maintains a balanced trade-off between sensitivity and specificity, which is critical in clinical settings where both false negatives and false positives carry significant risks. The ensemble architecture not only boosts classification performance butalso ensures adaptability to heterogeneous imaging patterns commonly found in real-world datasets. Beyond technical contributions, this study addresses critical deployment considerations such as integration with clinical information systems, potential for prospective real-world validation, and scalability to additional thoracic diseases. As such, it establishes a forward-looking framework that positions PELM as a practical, modular, and scalable solution for future integration into AI-powered radiology workflows. E.Y. : methodology, software, data curation, validation, formal analysis, investigation, visualization, writing--original draft preparation, writing--review and editing; F.H. : conceptualization, validation, writing--review and editing, supervision, project administration; K.A. : validation, writing--original draft preparation, writing--review and editing, formal analysis. All authors have read and agreed to the published version of the manuscript. This research was supported by ASELSAN Inc. The study did not require ethical approval. Not applicable. The authors used an open access dataset that is available from Kaggle CXR Pneumonia: [[https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia)]([https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia)) (accessed on 24 May 2025); PadChest: [[https://bimcv.cipf.es/bimcv-projects/padchest](https://bimcv.cipf.es/bimcv-projects/padchest)]([https://bimcv.cipf.es/bimcv-projects/padchest](https://bimcv.cipf.es/bimcv-projects/padchest)) (accessed on 24 May 2025); CXR-NIH: [[https://www.kaggle.com/datasets/nih-chest-xrays/data](https://www.kaggle.com/datasets/nih-chest-xrays/data)]([https://www.kaggle.com/datasets/nih-chest-xrays/data](https://www.kaggle.com/datasets/nih-chest-xrays/data)) (accessed on 24 May 2025); CheXpert: [[https://aimi.stanford.edu/datasets/chexpert-chest-x-rays](https://aimi.stanford.edu/datasets/chexpert-chest-x-rays)]([https://aimi.stanford.edu/datasets/chexpert-chest-x-rays](https://aimi.stanford.edu/datasets/chexpert-chest-x-rays)) (accessed on 24 May 2025). We thank the referee for his comprehensive review and appreciate his comments, corrections, and suggestions, which contributed significantly to improving the quality of the publication. The authors declare that this study received funding from ASELSAN Inc.