Deep learning-based applications for disease detection are essential tools for experts to effectively diagnose diseases at different stages. In this article, a new approach based on an evidence based fusion theory is proposed, allowing the combination of a set of deep learning classifiers to provide more accurate disease detection results. The main contribution of this work is the application of the Dempster–Shafer theory for the fusion of five pre trained convolutional neural networks including VGG16, Xception, InceptionV3, ResNet50, and DenseNet201 for the diagnosis of pneumonia from chest X-ray images. To evaluate this approach, experiments are conducted using a publicly available dataset containing more than 5800 chest X-ray images. The obtained results demonstrate that our approach provides excellent detection performance compared to other state-of-the-art methods; it achieves a precision of 97.5%, a recall of 98%, an f1-score of 97.8%, and an accuracy of 97.3%.

中文翻译：

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基于 Dempster-Shafer 理论的卷积神经网络融合用于胸部 X 射线图像的肺炎自动检测

基于深度学习的疾病检测应用程序是专家有效诊断不同阶段疾病的重要工具。在本文中，提出了一种基于证据的融合理论的新方法，允许组合一组深度学习分类器来提供更准确的疾病检测结果。这项工作的主要贡献是应用 Dempster-Shafer 理论融合了五个预训练的卷积神经网络，包括 VGG16、Xception、InceptionV3、ResNet50 和 DenseNet201，用于从胸部 X 射线图像诊断肺炎。为了评估这种方法，我们使用包含 5800 多张胸部 X 光图像的公开数据集进行实验。获得的结果表明，与其他最先进的方法相比，我们的方法提供了出色的检测性能；它达到了 97.5% 的准确率、98% 的召回率、97.8% 的 f1 分数和 97.3% 的准确率。