Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays

Himadri Mukherjee; Subhankar Ghosh; Ankita Dhar; Sk Md Obaidullah; K C Santosh; Kaushik Roy

doi:10.1007/s10489-020-01943-6

Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays

Appl Intell (Dordr). 2021;51(5):2777-2789. doi: 10.1007/s10489-020-01943-6. Epub 2020 Nov 6.

Authors

Himadri Mukherjee^#¹, Subhankar Ghosh², Ankita Dhar¹, Sk Md Obaidullah³, K C Santosh^#⁴, Kaushik Roy¹

Affiliations

¹ Department of Computer Science, West Bengal State University, West Bengal, India.
² CVPR Unit, Indian Statistical Institute, Kolkata, India.
³ Department of Computer Science, Engineering, Aliah University, Kolkata, India.
⁴ Department of Computer Science, The University of South Dakota, Vermillion, SD USA.

^# Contributed equally.

Abstract

Since December 2019, the novel COVID-19's spread rate is exponential, and AI-driven tools are used to prevent further spreading [1]. 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.

Keywords: COVID-19; Chest X-Ray; Computing tomography scan; Convolutional neural network; Deep neural network.