RESEARCH ARTICLE
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 Koubaa,
Henda Ben Ghézala,
Safa Ben Atitallah
RIADI Laboratory, University of Manouba, Manouba, Tunisia
Search for more papers by this authorMaha Driss
RIADI Laboratory, University of Manouba, Manouba, Tunisia
Security Engineering Lab, Prince Sultan University, Riyadh, Saudi Arabia
Search for more papers by this authorCorresponding Author
Wadii Boulila
RIADI Laboratory, University of Manouba, Manouba, Tunisia
Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh, Saudi Arabia
Correspondence
Wadii Boulila, RIADI Laboratory, University of Manouba, Manouba, Tunisia.
Email: [email protected]
Search for more papers by this authorAnis Koubaa
Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh, Saudi Arabia
Search for more papers by this authorHenda Ben Ghézala
RIADI Laboratory, University of Manouba, Manouba, Tunisia
Search for more papers by this authorSafa Ben Atitallah,
Maha Driss,
Wadii Boulila,
Anis Koubaa,
Henda Ben Ghézala,
Safa Ben Atitallah
RIADI Laboratory, University of Manouba, Manouba, Tunisia
Search for more papers by this authorMaha Driss
RIADI Laboratory, University of Manouba, Manouba, Tunisia
Security Engineering Lab, Prince Sultan University, Riyadh, Saudi Arabia
Search for more papers by this authorCorresponding Author
Wadii Boulila
RIADI Laboratory, University of Manouba, Manouba, Tunisia
Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh, Saudi Arabia
Correspondence
Wadii Boulila, RIADI Laboratory, University of Manouba, Manouba, Tunisia.
Email: [email protected]
Search for more papers by this authorAnis Koubaa
Robotics and Internet-of-Things Lab, Prince Sultan University, Riyadh, Saudi Arabia
Search for more papers by this authorHenda Ben Ghézala
RIADI Laboratory, University of Manouba, Manouba, Tunisia
Search for more papers by this authorAbstract
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%.
CONFLICT OF INTERESTS
The authors declare that they have no conflict of interest.
Open Research
DATA AVAILABILITY STATEMENT
Data will be available upon request to the corresponding author.
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