Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images
Abstract
Pneumonia is a leading cause of illness and death in children, underscoring the need for early and accurate detection. In this study, we propose a novel lightweight ensemble model for detecting pneumonia in children using chest X-ray images. This ensemble model integrates two pre-trained convolutional neural networks (CNNs), MobileNetV2 and NASNetMobile, selected for their balance of computational efficiency and accuracy. These models were fine-tuned on a pediatric chest X-ray dataset and combined to enhance classification performance. Our proposed ensemble model achieved a classification accuracy of 98.63%, significantly outperforming individual models such as MobileNetV2 (97.10%) and NASNetMobile(96.25%) in terms of accuracy, precision, recall, and F1 score. Moreover, the ensemble model outperformed state-of-the-art architectures, including ResNet50, InceptionV3, and DenseNet201, while maintaining computational efficiency. The proposed lightweight ensemble model presents a highly effective and resource-efficient solution for pneumonia detection, making it particularly suitable for deployment in resource-constrained settings.
Cite
@article{arxiv.2501.16249,
title = {Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images},
author = {Suresh Babu Nettur and Shanthi Karpurapu and Unnati Nettur and Likhit Sagar Gajja and Sravanthy Myneni and Akhil Dusi and Lalithya Posham},
journal= {arXiv preprint arXiv:2501.16249},
year = {2025}
}
Comments
Corresponding authors: Shanthi Karpurapu ([email protected]), Suresh Babu Nettur ([email protected]) Shanthi Karpurapu and Suresh Babu Nettur are co-first authors