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Potential diagnosis of COVID-19 from chest X-ray and CT findings using semi-supervised learning
- Source :
- Physical and Engineering Sciences in Medicine
- Publication Year :
- 2021
- Publisher :
- Springer International Publishing, 2021.
-
Abstract
- COVID-19 is an infectious disease, which has adversely affected public health and the economy across the world. On account of the highly infectious nature of the disease, rapid automated diagnosis of COVID-19 is urgently needed. A few recent findings suggest that chest X-rays and CT scans can be used by machine learning for the diagnosis of COVID-19. Herein, we employed semi-supervised learning (SSL) approaches to detect COVID-19 cases accurately by analyzing digital chest X-rays and CT scans. On a relatively small COVID-19 radiography dataset, which contains only 219 COVID-19 positive images, 1341 normal and 1345 viral pneumonia images, our algorithm, COVIDCon, which takes advantage of data augmentation, consistency regularization, and multicontrastive learning, attains 97.07% average class prediction accuracy, with 1000 labeled images, which is 7.65% better than the next best SSL method, virtual adversarial training. COVIDCon performs even better on a larger COVID-19 CT Scan dataset that contains 82,767 images. It achieved an excellent accuracy of 99.13%, at 20,000 labels, which is 6.45% better than the next best pseudo-labeling approach. COVIDCon outperforms other state-of-the-art algorithms at every label that we have investigated. These results demonstrate COVIDCon as the benchmark SSL algorithm for potential diagnosis of COVID-19 from chest X-rays and CT-Scans. Furthermore, COVIDCon performs exceptionally well in identifying COVID-19 positive cases from a completely unseen repository with a confirmed COVID-19 case history. COVIDCon, may provide a fast, accurate, and reliable method for screening COVID-19 patients.
- Subjects :
- Coronavirus disease 2019 (COVID-19)
Computer science
Radiography
Biomedical Engineering
Biophysics
Semi-supervised learning
Scientific Paper
Consistency (database systems)
Deep Learning
Deep neural networks
Diagnosis
Medical imaging
Humans
Radiology, Nuclear Medicine and imaging
Ct findings
Instrumentation
Radiological and Ultrasound Technology
business.industry
SARS-CoV-2
X-Rays
COVID-19
Pattern recognition
Class prediction
Benchmark (computing)
Artificial intelligence
Supervised Machine Learning
business
Tomography, X-Ray Computed
Biotechnology
Subjects
Details
- Language :
- English
- ISSN :
- 26624737 and 26624729
- Database :
- OpenAIRE
- Journal :
- Physical and Engineering Sciences in Medicine
- Accession number :
- edsair.doi.dedup.....f017d636210f2cdbff28da1cde9b2a8f