CovMnet-Deep Learning Model for classifying Coronavirus (COVID-19).
Malathy JawaharJani Anbarasi LVinayakumar RaviJ PrassannaS Graceline JasmineR ManikandanRames SekaranSuthendran KannanPublished in: Health and technology (2022)
Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.
Keyphrases
- deep learning
- coronavirus disease
- sars cov
- convolutional neural network
- high resolution
- respiratory syndrome coronavirus
- neural network
- artificial intelligence
- machine learning
- dual energy
- magnetic resonance imaging
- end stage renal disease
- healthcare
- emergency department
- peritoneal dialysis
- gene expression
- newly diagnosed
- ejection fraction
- optical coherence tomography
- risk assessment
- air pollution
- climate change
- electron microscopy
- human health
- adverse drug