Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays
Since December 2019, the novel COVID-19’s spread rate is exponential, and AI-driven tools are used to prevent further spreading . They can help predict, screen, and diagnose COVID-19 positive cases. Within this scope, imaging with Computed Tomography (CT) scans and Chest X-rays (CXRs) are widely used in mass triage situations. In the literature, AI-driven tools are limited to one data type either CT scan or CXR to detect COVID-19 positive cases. Integrating multiple data types could possibly provide more information in detecting anomaly patterns due to COVID-19. Therefore, in this paper, we engineered a Convolutional Neural Network (CNN) -tailored Deep Neural Network (DNN) that can collectively train/test both CT scans and CXRs. In our experiments, we achieved an overall accuracy of 96.28% (AUC = 0.9808 and false negative rate = 0.0208). Further, major existing DNNs provided coherent results while integrating CT scans and CXRs to detect COVID-19 positive cases.
Mukherjee, Himadri; Ghosh, Subhankar; Dhar, Ankita; Obaidullah, Sk Md; Santosh, K. C.; and Roy, Kaushik, "Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays" (2021). Journal Articles. 1990.
Open Access, Bronze