Biswas Shuvo, Mostafiz Rafid, Uddin Mohammad Shorif, Uddin Muhammad Shahin
Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh.
Institute of Information Technology, Noakhali Science and Technology University, Noakhali, Bangladesh.
PLoS One. 2025 Jul 17;20(7):e0324957. doi: 10.1371/journal.pone.0324957. eCollection 2025.
Pneumonia, a severe lung infection caused by various viruses, presents significant challenges in diagnosis and treatment due to its similarities with other respiratory conditions. Additionally, the need to protect patient privacy complicates the sharing of sensitive clinical data. This study introduces FLPneXAINet, an effective framework that combines federated learning (FL) with deep learning (DL) and explainable AI (XAI) to securely and accurately predict pneumonia using chest X-ray (CXR) images. We utilized a benchmark dataset from Kaggle, comprising 8,402 CXR images (3,904 normal and 4,498 pneumonia). The dataset was preprocessed and augmented using a cycle-consistent generative adversarial (CycleGAN) network to increase the volume of training data. Three pre-trained DL models named VGG16, NASNetMobile, and MobileNet were employed to extract features from the augmented dataset. Further, four ensemble DL (EDL) models were used to enhance feature extraction. Feature optimization was performed using recursive feature elimination (RFE), analysis of variance (ANOVA), and random forest (RF) to select the most relevant features. These optimized features were then inputted into machine learning (ML) models, including K-nearest neighbor (KNN), naive bayes (NB), support vector machine (SVM), and RF, for pneumonia prediction. The performance of the models was evaluated in a FL environment, with the EDL network achieving the best results: accuracy 97.61%, F1 score 98.36%, recall 98.13%, and precision 98.59%. The framework's predictions were further validated using two XAI techniques-Local Interpretable Model-Agnostic Explanations (LIME) and Grad-CAM. FLPneXAINet offers a robust solution for healthcare professionals to accurately diagnose pneumonia, ensuring timely treatment while safeguarding patient privacy.
肺炎是由多种病毒引起的严重肺部感染，因其与其他呼吸道疾病相似，在诊断和治疗方面面临重大挑战。此外，保护患者隐私的需求使敏感临床数据的共享变得复杂。本研究引入了FLPneXAINet，这是一个将联邦学习（FL）与深度学习（DL）和可解释人工智能（XAI）相结合的有效框架，用于使用胸部X光（CXR）图像安全、准确地预测肺炎。我们使用了来自Kaggle的基准数据集，其中包括8402张CXR图像（3904张正常图像和4498张肺炎图像）。使用循环一致生成对抗（CycleGAN）网络对数据集进行预处理和扩充，以增加训练数据量。使用三个预训练的DL模型VGG16、NASNetMobile和MobileNet从扩充数据集中提取特征。此外，使用四个集成DL（EDL）模型来增强特征提取。使用递归特征消除（RFE）、方差分析（ANOVA）和随机森林（RF）进行特征优化，以选择最相关的特征。然后将这些优化后的特征输入到机器学习（ML）模型中，包括K近邻（KNN）、朴素贝叶斯（NB）、支持向量机（SVM）和RF，用于肺炎预测。在FL环境中评估了模型的性能，EDL网络取得了最佳结果：准确率97.61%，F1分数98.36%，召回率98.13%，精确率98.59%。使用两种XAI技术——局部可解释模型无关解释（LIME）和Grad-CAM对框架的预测进行了进一步验证。FLPneXAINet为医疗保健专业人员提供了一个强大的解决方案，以准确诊断肺炎，确保及时治疗，同时保护患者隐私。
