Lightweight weighted average ensemble model for pneumonia detection in chest X-ray images

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. Our main contribution lies in the development of a novel, particularly weighted average ensemble model that combines two lightweight pre-trained convolutional neural networks (CNNs), MobileNetV2 and NASNetMobile, an ensemble combination that has not been previously explored in the field of deep learning for image classification tasks. These models were selected for their balance of computational efficiency and accuracy, fine-tuned on a pediatric chest X-ray dataset, and combined to enhance classification performance. The 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 weighted average ensemble model presents a highly effective and resource-efficient solution for pneumonia detection, making it particularly suitable for deployment in resource-constrained settings.