Machine learning based COVID -19 disease recognition using CT images of SIRM database

Abstract

The COVID-19 pandemic, probably one of the most widespread pandemics humanity has encountered in the twenty first century, caused death to almost 1.75 M people worldwide, impacting almost 80 M lives with direct contact. In order to contain the spread of coronavirus, it is necessary to develop a reliant and quick method to identify those who are affected and isolate them until full recovery is made. The imagery knowledge has been shown to be useful for quick COVID-19 diagnosis. Though the scans of computational tomography (CT) demonstrate a range of viral infection signals, considering the vast number of images, certain visual characteristics are challenging to distinguish and can take a long time to be identified by radiologists. In this study for detection of the COVID-19, a dataset is formed by taking 3764 images. The feature extraction process is applied to the dataset to increase the classification performance. Techniques like Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used for feature extraction. Then various machine learning algorithms applied such as Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Multi- Level Perceptron, Naive Bayes, K-Nearest Neighbours and Random Forests are used for classification of COVID-19 disease detection. Sensitivity, Specificity, Accuracy, Precision, and F-score are the metrics used to measure the performance of different machine learning models. Among these machine learning models SVM with GLCM as feature extraction technique using 10-fold cross validation gives the best classification result with 99.70% accuracy, 99.80% sensitivity and 97.03% F-score. We also ran these tests on different data sets and found that the results are similar across those too, as discussed later in the results section.

Keywords:

• Classification
• coronavirus
• COVID-19
• CT images
• feature extraction

Acknowledgements

This study was not funded by any agencies. Author Saroj Kumar Pandey declares that he has no conflict of interest. Author Rekh Ram Janghel, Pankaj Kumar Mishra, and Rachana Kaabra declares that they have no conflict of interest. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

Disclosure statement

No potential conflict of interest was reported by the author(s).