Collaborative Consultation Doctors Model: Unifying CNN and ViT for COVID-19 Diagnostic

Author(s)

Trong-Thuan Nguyen (0000-0001-7729-2927), Vietnam National University
Tam Nguyen (0000-0003-0236-7992), University of DaytonFollow
Minh-Triet Tran (0000-0003-3046-3041), Vietnam National UniversityFollow

Document Type

Article

Publication Date

2023

Publication Source

IEEE Access

Abstract

The COVID-19 pandemic presents significant challenges due to its high transmissibility and mortality risk. Traditional diagnostic methods, such as RT-PCR, have limitations that hinder timely and accurate screening. In response, AI-powered computer-aided imaging analysis techniques have emerged as a promising alternative for COVID-19 diagnosis. In this paper, we propose a novel approach that combines the strengths of Convolutional Neural Network (CNN) and Vision Transformer (ViT) to enhance the performance of COVID-19 diagnosis models. CNN excels at capturing spatial features in medical images, while ViT leverages self-attention mechanisms inspired by human radiologists. Additionally, our approach draws inspiration from subclinical diagnosis, a collaborative process involving attending physicians and specialists, which has proven effective in achieving accurate and comprehensive diagnoses. To this end, we employ an early fusion strategy integrating CNN and ViT, then fed into a residual neural network. By fusing these complementary features, our approach achieves state-of-the-art performance in accurately identifying COVID-19 cases on two benchmark datasets: Chest X-ray and Clean-CC-CCII. This research has the potential to enable timely and accurate screening, aiding in the early detection and management of COVID-19 cases. Our findings contribute to the growing knowledge of AI-powered diagnostic techniques and demonstrate the potential for advanced imaging analysis methods to support medical professionals in combating the ongoing pandemic.

Inclusive pages

95346-95357

ISBN/ISSN

2169-3536

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1109/ACCESS.2023.3307014

Publisher

IEEE-INST Electrical Electronics Engineers INC

Volume

11

Peer Reviewed

yes

eCommons Citation

Nguyen, Trong-Thuan; Nguyen, Tam; and Tran, Minh-Triet, "Collaborative Consultation Doctors Model: Unifying CNN and ViT for COVID-19 Diagnostic" (2023). Computer Science Faculty Publications. 196.
https://ecommons.udayton.edu/cps_fac_pub/196