Probabilistic combination of eigenlungs-based classifiers for COVID-19 diagnosis in chest CT images

• URL: http://arxiv.org/abs/2103.02961v1
• Date: Thu, 4 Mar 2021 11:30:38 GMT
• Title: Probabilistic combination of eigenlungs-based classifiers for COVID-19 diagnosis in chest CT images
• Authors: Juan E. Arco, Andr\'es Ortiz, Javier Ram\'irez, Francisco J. Mart\'inez-Murcia, Yu-Dong Zhang, Jordi Broncano, M. \'Alvaro Berb\'is, Javier Royuela-del-Val, Antonio Luna, Juan M. G\'orriz
• Abstract summary: More than 100 million confirmed cases of COVID-19, including more than 2.4 million deaths. The use of medical imaging such as chest X-ray (CXR) and chest Computed Tomography (CCT) have proved to be an excellent solution. We propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify pneumonia patterns.
• Score: 6.1020196190084555
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The outbreak of the COVID-19 (Coronavirus disease 2019) pandemic has changed the world. According to the World Health Organization (WHO), there have been more than 100 million confirmed cases of COVID-19, including more than 2.4 million deaths. It is extremely important the early detection of the disease, and the use of medical imaging such as chest X-ray (CXR) and chest Computed Tomography (CCT) have proved to be an excellent solution. However, this process requires clinicians to do it within a manual and time-consuming task, which is not ideal when trying to speed up the diagnosis. In this work, we propose an ensemble classifier based on probabilistic Support Vector Machine (SVM) in order to identify pneumonia patterns while providing information about the reliability of the classification. Specifically, each CCT scan is divided into cubic patches and features contained in each one of them are extracted by applying kernel PCA. The use of base classifiers within an ensemble allows our system to identify the pneumonia patterns regardless of their size or location. Decisions of each individual patch are then combined into a global one according to the reliability of each individual classification: the lower the uncertainty, the higher the contribution. Performance is evaluated in a real scenario, yielding an accuracy of 97.86%. The large performance obtained and the simplicity of the system (use of deep learning in CCT images would result in a huge computational cost) evidence the applicability of our proposal in a real-world environment.

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