Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images

TL;DR

This paper introduces a lightweight ensemble of MobileNetV2 and NASNetMobile CNNs that achieves high accuracy in pneumonia detection from pediatric chest X-ray images, suitable for resource-limited environments.

Contribution

A novel lightweight ensemble model combining two pre-trained CNNs for improved pneumonia detection accuracy in chest X-ray images.

Findings

Achieved 98.63% accuracy, outperforming individual models.

Outperformed state-of-the-art architectures in accuracy and efficiency.

Suitable for deployment in resource-constrained settings.

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.