A novel deep learning algorithm to process COVID-19 chest x-rays

Dong Xie; Arthur C. Depoian II; Colleen P. Bailey; Parthasarathy Guturu

doi:10.1117/12.2619141

3 June 2022 A novel deep learning algorithm to process COVID-19 chest x-rays

Dong Xie, Arthur C. Depoian II, Colleen P. Bailey, Parthasarathy Guturu

Proceedings Volume 12104, Anomaly Detection and Imaging with X-Rays (ADIX) VII; 121040G (2022) https://doi.org/10.1117/12.2619141
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Chest X-rays can quickly assess the COVID-19 status of test subjects and address the problem of inadequate medical resources in emergency departments and centers. The image classification model established by the deep learning method of artificial intelligence can help doctors make a better judgment on patients with COVID-19 and related lung diseases. We compared and analyzed the current popular deep learning image classification methods, VGGNet, GoogleNet, and ResNet, using publicly available chest X-ray datasets on COVID-19 from different organizations. According to the characteristics of chest X-ray images and the classification results of the deep learning algorithm, a novel image classification algorithm, CovidXNet, is proposed. Based on the ResNet model, the CovidXNet algorithm introduces the hard sample memory pool method to improve the accuracy and generalization of the algorithm. CovidXNet is able to categorize chest X-ray images more efficiently and accurately than other popular image classification algorithms, allowing doctors to quickly confirm the patient’s diagnosis.

Citation Download Citation

Dong Xie, Arthur C. Depoian II, Colleen P. Bailey, and Parthasarathy Guturu "A novel deep learning algorithm to process COVID-19 chest x-rays", Proc. SPIE 12104, Anomaly Detection and Imaging with X-Rays (ADIX) VII, 121040G (3 June 2022); https://doi.org/10.1117/12.2619141

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available