可解释的深度学习用于胸片图像中的儿童肺炎检测
Explainable Deep Learning for Pediatric Pneumonia Detection in Chest X-Ray Images
AI总结：
本研究比较了两种CNN模型用于儿童肺炎检测，发现EfficientNet-B0在性能和计算效率上更优，结合可解释性技术提升了诊断的透明度和可信度。
摘要：
背景：肺炎仍然是全球范围内导致儿童发病率和死亡率的主要原因，强调了需要准确且高效的诊断支持工具。深度学习在医学图像分析中显示出强大的潜力，尤其是在胸片解读方面。本研究比较了两种最先进的卷积神经网络（CNN）架构，用于自动化儿童肺炎检测。方法：使用了一个公开的包含5,863张儿童胸片图像的数据集。图像通过归一化、调整大小和数据增强进行预处理，以提高泛化能力。DenseNet121和EfficientNet-B0在相同的训练设置下使用预训练的ImageNet权重进行微调。性能通过准确率、F1分数、马修相关系数（MCC）和召回率进行评估。模型可解释性通过梯度加权类激活映射（Grad-CAM）和局部可解释性模型无关解释（LIME）来整合，以可视化影响预测的图像区域。结果：EfficientNet-B0优于DenseNet121，准确率为84.6%，F1分数为0.8899，MCC为0.6849。DenseNet121的准确率为79.7%，F1分数为0.8597，MCC为0.5852。两个模型都表现出高于0.99的召回率，表明对肺炎检测的高灵敏度。Grad-CAM和LIME可视化显示，模型对临床相关肺部区域有持续的关注，支持模型决策的可靠性。结论：EfficientNet-B0相比DenseNet121提供了更平衡且计算更高效的性能，使其成为临床部署的有力候选者。可解释性技术的整合增强了AI辅助儿童肺炎诊断的透明度和可信度。
英文摘要：
Background: Pneumonia remains a leading cause of morbidity and mortality among children worldwide, emphasizing the need for accurate and efficient diagnostic support tools. Deep learning has shown strong potential in medical image analysis, particularly for chest X-ray interpretation. This study compares two state-of-the-art convolutional neural network (CNN) architectures for automated pediatric pneumonia detection. Methods: A publicly available dataset of 5,863 pediatric chest X-ray images was used. Images were preprocessed through normalization, resizing, and data augmentation to enhance generalization. DenseNet121 and EfficientNet-B0 were fine-tuned using pretrained ImageNet weights under identical training settings. Performance was evaluated using accuracy, F1-score, Matthews Correlation Coefficient (MCC), and recall. Model explainability was incorporated using Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-agnostic Explanations (LIME) to visualize image regions influencing predictions. Results: EfficientNet-B0 outperformed DenseNet121, achieving an accuracy of 84.6%, F1-score of 0.8899, and MCC of 0.6849. DenseNet121 achieved 79.7% accuracy, an F1-score of 0.8597, and MCC of 0.5852. Both models demonstrated high recall values above 0.99, indicating strong sensitivity to pneumonia detection. Grad-CAM and LIME visualizations showed consistent focus on clinically relevant lung regions, supporting the reliability of model decisions. Conclusions: EfficientNet-B0 provided a more balanced and computationally efficient performance compared to DenseNet121, making it a strong candidate for clinical deployment. The integration of explainability techniques enhances transparency and trustworthiness in AI-assisted pediatric pneumonia diagnosis.
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