A study on the suitability of different pooling operators for convolutional neural networks in the prediction of COVID-19 through chest x-ray image analysis
| dc.contributor.author | Rodríguez Martínez, Iosu | |
| dc.contributor.author | Ursúa Medrano, Pablo | |
| dc.contributor.author | Fernández Fernández, Francisco Javier | |
| dc.contributor.author | Takáč, Zdenko | |
| dc.contributor.author | Bustince Sola, Humberto | |
| dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
| dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
| dc.contributor.funder | Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa | es |
| dc.date.accessioned | 2023-11-06T18:34:28Z | |
| dc.date.available | 2023-11-06T18:34:28Z | |
| dc.date.issued | 2024 | |
| dc.date.updated | 2023-11-06T18:18:40Z | |
| dc.description.abstract | The 2019 coronavirus disease outbreak, caused by the severe acute respiratory syndrome type-2 virus (SARS-CoV-2), was declared a pandemic in March 2020. Since its emergence to the present day, this disease has brought multiple countries to the brink of health care collapse during several waves of the disease. One of the most common tests performed on patients is chest x-ray imaging. These images show the severity of the patient's illness and whether it is indeed covid or another type of pneumonia. Automated assessment of this type of imaging could alleviate the time required for physicians to treat and diagnose each patient. To this end, in this paper we propose the use of Convolutional Neural Networks (CNNs) to carry out this process. The aim of this paper is twofold. Firstly, we present a pipeline adapted to this problem, covering all steps from the preprocessing of the datasets to the generation of classification models based on CNNs. Secondly, we have focused our study on the modification of the information fusion processes of this type of architectures, in the pooling layers. We propose a number of aggregation theory functions that are suitable to replace classical processes and have shown their benefits in past applications, and study their performance in the context of the x-ray classification problem. We find that replacing the feature reduction processes of CNNs leads to drastically different behaviours of the final model, which can be beneficial when prioritizing certain metrics such as precision or recall. | en |
| dc.description.sponsorship | The authors gratefully acknowledge the financial support of Tracasa Instrumental (iTRACASA), Spain and of the Gobierno de Navarra - Departamento de Universidad, Innovación y Transformación Digital, Spain, as well as that of the Spanish Ministry of Science (project PID2022-136627NB-I00) and the project PC095-096 FUSIPROD. Z. Takáč is supported by grant VEGA, Slovak Republic 1/0267/21. Open access funding provided by Universidad Pública de Navarra. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Rodriguez-Martinez, I., Ursua-Medrano, P., Fernandez, J., Takac, Z., Bustince, H. (2024) A study on the suitability of different pooling operators for convolutional neural networks in the prediction of COVID-19 through chest x-ray image analysis. Expert Systems with Applications, 235, 1-11. https://doi.org/10.1016/j.eswa.2023.121162. | en |
| dc.identifier.doi | 10.1016/j.eswa.2023.121162 | |
| dc.identifier.issn | 0957-4174 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/46685 | |
| dc.language.iso | eng | en |
| dc.publisher | Elsevier | en |
| dc.relation.ispartof | Expert Systems With Applications 235, (2024), 121162 | en |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI//PID2022-136627NB-I00/ | |
| dc.relation.projectID | info:eu-repo/grantAgreement/Gobierno de Navarra//PC095-096 FUSIPROD/ | |
| dc.relation.publisherversion | https://doi.org/10.1016/j.eswa.2023.121162 | |
| dc.rights | © 2023 The Author(s). This is an open access article under the CC BY license. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Aggregation functions | en |
| dc.subject | Convolutional neural networks | en |
| dc.subject | COVID-19 | en |
| dc.subject | Pooling functions | en |
| dc.subject | SARS-CoV-2 | en |
| dc.title | A study on the suitability of different pooling operators for convolutional neural networks in the prediction of COVID-19 through chest x-ray image analysis | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | info:eu-repo/semantics/publishedVersion | |
| dspace.entity.type | Publication | |
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