ABSTRACT
The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. Author Mohammad Khishe disagrees with the retraction. The other authors have not responded to correspondence regarding this retraction. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
ABSTRACT
With growing demands for diagnosing COVID-19 definite cases, employing radiological images, i.e., the chest X-ray, is becoming challenging. Deep Convolutional Neural Networks (DCNN) propose effective automated models to detect COVID_19 positive cases. In order to improve the total accuracy, this paper proposes using the novel Trigonometric Function (TF) instead of the existing gradient descendent-based training method for training fully connected layers to have a COVID-19 detector with parallel implementation ability. The designed model gets then benchmarked on a verified dataset denominated COVID-Xray-5k. The results get investigated by qualified research with classic DCNN, BWC, and MSAD. The results confirm that the produced detector can present competitive results compared to the benchmark detection models. The paper also examines the class activation map theory to detect the areas probably infected by the Covid-19 virus. As experts confirm, the obtained results get correlated with the clinical recognitions. © 2023 The Royal Photographic Society.