Enhancing COVID-19 Detection: An Xception-Based Model with Advanced Transfer Learning from X-ray Thorax Images
Reagan E. Mandiya, Hervé M. Kongo, Selain K. Kasereka, Kyandoghere Kyamakya, Petro M. Tshakwanda, Nathanaël M. Kasoro
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.