Enhancing COVID-19 Detection: An Xception-Based Model with Advanced Transfer Learning from X-ray Thorax Images

Author:

Mandiya Reagan E.12,Kongo Hervé M.1,Kasereka Selain K.12ORCID,Kyandoghere Kyamakya3ORCID,Tshakwanda Petro Mushidi4ORCID,Kasoro Nathanaël M.12

Affiliation:

1. Mathematics, Statistics and Computer Science Department, University of Kinshasa, Kinshasa XI P.O. Box 190, Democratic Republic of the Congo

2. Artificial Intelligence, Big Data and Modeling Simulation Research Center (ABIL), Kinshasa XI P.O. Box 190, Democratic Republic of the Congo

3. Institute of Smart Systems Technologies, University of Klagenfurt, 9020 Klagenfurt am Wörthersee, Austria

4. Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA

Abstract

Rapid and precise identification of Coronavirus Disease 2019 (COVID-19) is pivotal for effective patient care, comprehending the pandemic’s trajectory, and enhancing long-term patient survival rates. Despite numerous recent endeavors in medical imaging, many convolutional neural network-based models grapple with the expressiveness problem and overfitting, and the training process of these models is always resource-intensive. This paper presents an innovative approach employing Xception, augmented with cutting-edge transfer learning techniques to forecast COVID-19 from X-ray thorax images. Our experimental findings demonstrate that the proposed model surpasses the predictive accuracy of established models in the domain, including Xception, VGG-16, and ResNet. This research marks a significant stride toward enhancing COVID-19 detection through a sophisticated and high-performing imaging model.

Publisher

MDPI AG

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