Document Type
Article
Publication Date
12-2022
Publication Source
Journal of Imaging
Abstract
The application of chest X-ray imaging for early disease screening is attracting interest from the computer vision and deep learning community. To date, various deep learning models have been applied in X-ray image analysis. However, models perform inconsistently depending on the dataset. In this paper, we consider each individual model as a medical doctor. We then propose a doctor consultation-inspired method that fuses multiple models. In particular, we consider both early and late fusion mechanisms for consultation. The early fusion mechanism combines the deep learned features from multiple models, whereas the late fusion method combines the confidence scores of all individual models. Experiments on two X-ray imaging datasets demonstrate the superiority of the proposed method relative to baseline. The experimental results also show that early consultation consistently outperforms the late consultation mechanism in both benchmark datasets. In particular, the early doctor consultation-inspired model outperforms all individual models by a large margin, i.e., 3.03 and 1.86 in terms of accuracy in the UIT COVID-19 and chest X-ray datasets, respectively.
ISBN/ISSN
2313-433X
Document Version
Published Version
Publisher
MDPI
Volume
8
Peer Reviewed
yes
Issue
12
eCommons Citation
Phung, Kim Anh; Nguyen, Thuan Trong; Wangad, Nileshkumar; Baraheem, Samah; Vo, Nguyen D.; and Nguyen, Khang, "Disease Recognition in X-Ray Images with Doctor Consultation-Inspired Model" (2022). Computer Science Faculty Publications. 202.
https://ecommons.udayton.edu/cps_fac_pub/202
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/10.3390/jimaging8120323