Delaunay triangulation based intelligent system for the diagnosis of covid from the low radiation CXR images.

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Abstract

Covid-19 is a viral infection that causes a profound impact on the lives of the World population. It is a global pandemic spreading across the world in a faster way. It made a global impact on the health, economy, and education system in all the countries. As it is a rapidly spreading disease, prevention demands a fast and accurate diagnosis system. In a highly densely populated country, the demand for fast and affordable early diagnosis is required to reduce the disaster. Within this diagnosis time, the infection spreads rapidly and worsens the infected person’s status. To provide a faster and more affordable early diagnosis of covid, posterior-anterior chest radiographs (CXR) are used. Diagnosis of covid from CXR is challenging due to the images’ interclass similarity and intraclass variation. This study proposes a deep learning-based robust early diagnosis method for covid. To balance the intraclass variation and interclass similarity in CXR images, the deep fused Delaunay triangulation (DT) is proposed as the CXR has low radiation and unbalanced quality images. The deep features are to be extracted to increase the robustness of the diagnosis method. Without segmentation, the proposed DT algorithm achieves the accurate visualization of the suspicious region in the CXR. The proposed model is trained and tested by the largest benchmark covid-19 radiology dataset with 3616 covid CXR images and 3500 standard CXR images. The performance of the proposed system is analyzed in terms of accuracy, sensitivity, specificity, and AUC. The proposed system yields the highest validation accuracy.© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.