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Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images
- Source :
- Signal, Image and Video Processing
- Publication Year :
- 2021
- Publisher :
- Springer London, 2021.
-
Abstract
- The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.
- Subjects :
- 2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19)
Computer science
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
02 engineering and technology
Deep convolution neural network
Convolutional neural network
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
0202 electrical engineering, electronic engineering, information engineering
Electrical and Electronic Engineering
SARS
Original Paper
Contextual image classification
business.industry
Deep learning
Chest X-ray
COVID-19
Pattern recognition
Transfer learning
Signal Processing
X ray image
020201 artificial intelligence & image processing
Artificial intelligence
business
Transfer of learning
Subjects
Details
- Language :
- English
- ISSN :
- 18631711 and 18631703
- Database :
- OpenAIRE
- Journal :
- Signal, Image and Video Processing
- Accession number :
- edsair.doi.dedup.....0202d610e95aab32af38bd745fb18708