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

• URL: http://arxiv.org/abs/2501.16249v2
• Date: Sat, 01 Feb 2025 00:54:20 GMT
• Title: Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images
• Authors: Suresh Babu Nettur, Shanthi Karpurapu, Unnati Nettur, Likhit Sagar Gajja, Sravanthy Myneni, Akhil Dusi, Lalithya Posham,
• Abstract summary: Pneumonia is a leading cause of illness and death in children, underscoring the need for early and accurate detection. We propose a novel lightweight ensemble model for detecting pneumonia in children using chest X-ray images.
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• 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.

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