Machine Learning Analysis of Chest CT Scan Images as a Complementary Digital Test of Coronavirus (COVID-19) Patients
Al-karawi, D.; Al-Zaidi, S.; Polus, N.; Jassim, S.
Show abstract
This paper reports on the development and performance of machine learning schemes for the analysis of Chest CT Scan images of Coronavirus COVID-19 patients and demonstrates significant success in efficiently and automatically testing for COVID-19 infection. In particular, an innovative frequency domain algorithm, to be called FFT-Gabor scheme, will be shown to predict in almost real-time the state of the patient with an average accuracy of 95.37%, sensitivity 95.99% and specificity 94.76%. The FFT-Gabor scheme is adequately informative in that clinicians can visually examine the FFT-Gabor feature to support their final diagnostic. Key StrengthsThe proposed FFT-Gabor scheme is an automatic machine learning scheme that works in real time and achieves significantly high accuracy with very low false negative, and can provide supporting evidences of the predicted decision by visually displaying the final features upon which decision is made. This scheme will be most beneficial when used in addition to the RT-PCR swab test of non-symptomatic cases.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Hilbert-Envelope Features for Cardiac Disease Classification from Noisy Phonocardiograms 95%
- Improved online event detection and differentiation by a simple gradient-based nonlinear transformation: Implications for the biomedical signal and image analysis 93%
- Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification 93%
Similar papers in this journal
- An Inexpensive Smartphone-Based Device and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes 94%
- DermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation 93%
- Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence 92%
Similar papers in this journal
- Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation 96%
- COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images 95%
- Development and Clinical Validation of Swaasa AI Platform for screening and prioritization of Pulmonary TB 95%
Similar papers in this journal
- Improving Tuberculosis Detection in Chest X-ray Images through Transfer Learning and Deep Learning: A Comparative Study of CNN Architectures 94%
- Predicting COVID-19 Pandemic in Saudi Arabia Using Modified Singular Spectrum Analysis 92%
- Prediction of COVID-19 Mortality to Support Patient Prognosis and Triage and Limits of Current Open-Source Data 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.