Fusion of convolutional neural networks based on Dempster–Shafer theory for automatic pneumonia detection from chest X‐ray images
Safa Ben Atitallah, Maha Driss, Wadii Boulila, Anis Koubâa, Henda Ben Ghézala
Abstract
Abstract 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%.