一种用于从胸部 X 光图像中识别肺炎的混合可解释集成式变压器编码器。

INTRODUCTION: Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance. OBJECTIVES: A new hybrid explainable deep learning framework is proposed for accurate pneumonia disease identification using chest X-ray images. METHODS: The proposed hybrid workflow is developed by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism. The ensemble learning backbone is used to extract strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, VGG16, and GoogleNet) and ensemble B (i.e., DenseNet201, InceptionResNetV2, and Xception). Whereas, the Transformer Encoder is built based on the self-attention mechanism with multilayer perceptron (MLP) for accurate disease identification. The visual explainable saliency maps are derived to emphasize the crucial predicted regions on the input X-ray images. The end-to-end training process of the proposed deep learning models over all scenarios is performed for binary and multi-class classification scenarios. RESULTS: The proposed hybrid deep learning model recorded 99.21% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 98.19% accuracy and 97.29% F1-score for multi-classification task. For the ensemble binary identification scenario, ensemble A recorded 97.22% accuracy and 97.14% F1-score, while ensemble B achieved 96.44% for both accuracy and F1-score. For the ensemble multiclass identification scenario, ensemble A recorded 97.2% accuracy and 95.8% F1-score, while ensemble B recorded 96.4% accuracy and 94.9% F1-score. CONCLUSION: The proposed hybrid deep learning framework could provide promising and encouraging explainable identification performance comparing with the individual, ensemble models, or even the latest AI models in the literature. The code is available here: https://github.com/chiagoziemchima/Pneumonia_Identificaton.

简介：肺炎是一种微生物感染，会导致人体肺部细胞慢性炎症。胸部 X 光成像是最常用于早期检测肺炎的筛查方法。虽然胸部 X 光图像通常比较模糊且光照较低，但需要一种强大的特征提取方法才能实现有前景的识别性能。

目的：提出了一种新的混合可解释深度学习框架，用于使用胸部 X 光图像准确识别肺炎疾病。

方法：所提出的混合工作流程是通过融合集成卷积网络和变压器编码器机制的功能来开发的。在两种不同情况下，使用集成学习骨干从原始输入 X 光图像中提取强特征：集成 A（即 DenseNet201、VGG16 和 GoogleNet）和集成 B（即 DenseNet201、InceptionResNetV2 和 Xception）。而变压器编码器则是基于自注意力机制和多层感知机（MLP）构建的，用于准确识别疾病。导出可视解释性显着性图，以强调输入 X 光图像上的关键预测区域。对所有场景进行端到端训练过程，以进行二进制和多类分类场景。

结果：在二进制分类任务中，所提出的混合深度学习模型的整体准确率和 F1 得分为 99.21%，在多类分类任务中准确率和 F1 得分为 98.19%和 97.29%。对于集成二进制识别场景，集成 A 记录的准确率和 F1 得分分别为 97.22%和 97.14%，而集成 B 则分别为 96.44%和 96.44%。对于集成多类识别场景，集成 A 记录的准确率和 F1 得分分别为 97.2%和 95.8%，而集成 B 记录的准确率和 F1 得分分别为 96.4%和 94.9%。

结论：与个体、集成模型甚至文献中的最新 AI 模型相比，所提出的混合深度学习框架可以提供有前景和令人鼓舞的可解释识别性能。代码可在此处获得：https://github.com/chiagoziemchima/Pneumonia_Identificaton。