IEEE/ACM Trans Comput Biol Bioinform. 2024 Jul-Aug;21(4):857-868. doi: 10.1109/TCBB.2022.3190265. Epub 2024 Aug 8.
Pneumonia mainly refers to lung infections caused by pathogens, such as bacteria and viruses. Currently, deep learning methods have been applied to identify pneumonia. However, the traditional deep learning methods for pneumonia identification take less account of the influence of the lung X-ray image background on the model's testing effect, which limits the improvement of the model's accuracy. In this paper, we propose a deep learning method that considers image background factors and analyzes the proposed method with explainable deep learning for explainability. The essential idea is to remove the image background, improve the pneumonia recognition accuracy, and apply the Grad-CAM method to obtain an explainable deep learning model for pneumonia identification. In the proposed approach, (1) preliminary deep learning models for pneumonia X-ray image identification without considering the background are built; (2) deep learning models for pneumonia X-ray image identification with background consideration are built to improve the accuracy of pneumonia identification; (3) Grad-CAM method is employed to analyze the explainability. The proposed approach improves the accuracy of pneumonia identification, and the highest accuracy of VGG16 reaches 95.6%. The proposed approach can be applied to real pneumonia identification for early detection and treatment.
肺炎主要是指由细菌和病毒等病原体引起的肺部感染。目前，深度学习方法已被应用于识别肺炎。然而，传统的肺炎识别深度学习方法较少考虑肺部 X 光图像背景对模型测试效果的影响，这限制了模型精度的提高。在本文中，我们提出了一种考虑图像背景因素的深度学习方法，并通过可解释性深度学习对所提出的方法进行了分析，以实现可解释性。其基本思想是去除图像背景，提高肺炎识别准确率，并应用 Grad-CAM 方法获取用于肺炎识别的可解释深度学习模型。在提出的方法中，（1）构建了不考虑背景的肺炎 X 光图像识别初步深度学习模型；（2）构建了考虑背景的肺炎 X 光图像识别深度学习模型，以提高肺炎识别的准确率；（3）采用 Grad-CAM 方法进行可解释性分析。提出的方法提高了肺炎识别的准确率，VGG16 的最高准确率达到 95.6%。提出的方法可应用于实际的肺炎识别，以实现早期检测和治疗。
