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Published in: International Journal of Multimedia Information Retrieval 1/2021

20-02-2021 | Regular Paper

Design ensemble deep learning model for pneumonia disease classification

Author: Khalid El Asnaoui

Published in: International Journal of Multimedia Information Retrieval | Issue 1/2021

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Abstract

With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).

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Metadata
Title
Design ensemble deep learning model for pneumonia disease classification
Author
Khalid El Asnaoui
Publication date
20-02-2021
Publisher
Springer London
Published in
International Journal of Multimedia Information Retrieval / Issue 1/2021
Print ISSN: 2192-6611
Electronic ISSN: 2192-662X
DOI
https://doi.org/10.1007/s13735-021-00204-7

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