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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\u2013Shafer 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\u2010ray images. To evaluate this approach, experiments are conducted using a publicly available dataset containing more than 5800 chest X\u2010ray images. 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