轻量级集成模型提升儿童肺炎胸片检测准确率
Lightweight Weighted Average Ensemble Model for Pneumonia Detection in Chest X-Ray Images
- 用MobileNetV2与NASNetMobile集成，兼顾效率与精度
- 准确率达98.63%，显著优于单模型和主流网络
- 适合在计算资源有限的基层医疗场景部署
肺炎是儿童患病和死亡的主要原因，亟需早期精准检测。本研究提出一种轻量级集成模型，用于儿童胸片中的肺炎检测。该模型融合两个预训练卷积神经网络（CNN）——MobileNetV2与NASNetMobile，二者在儿科胸片数据集上微调后集成，以提升分类性能。所提集成模型达到98.63%的分类准确率，在准确率、精确率、召回率和F1分数上均显著优于MobileNetV2（97.10%）和NASNetMobile（96.25%）。此外，该模型在保持计算高效的同时，超越了ResNet50、InceptionV3和DenseNet201等先进架构。该轻量级集成模型为肺炎检测提供了高效且资源友好的解决方案，特别适用于计算资源受限的医疗环境。
原文摘要 · Abstract (English)
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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