Practical Machine Learning Techniques for COVID-19 Detection Using Chest X-Ray Images

This paper presents effective techniques for automatic detection/classification
of COVID-19 and other lung diseases using machine learning, including
deep learning with convolutional neural networks (CNN) and classical machine
learning techniques. We had access to a large number of chest X-ray images to
use as input data. The data contains various categories including COVID-19,
Pneumonia, Pneumothorax, Atelectasis, and Normal (without disease). In addition,
chest X-ray images with many findings (abnormalities and diseases) from
the National Institutes of Health (NIH) was also considered. Our deep learning
approach used a CNN architecture with VGG16 and VGG19 models which were
pre-trained with ImageNet. We compared this approach with the classical machine
learning approaches, namely Support Vector Machine (SVM) and Random Forest.
In addition to independently extracting image features, pre-trained features
obtained from a VGG19 model were utilized with these classical machine learning
techniques. Both binary and categorical (multi-class) classification tasks were
considered on classical machine learning and deep learning. Several X-ray images
ranging from 7000 images up to 11500 images were used in each of our experiments.
Five experimental cases were considered for each classification model.
Results obtained from all techniques were evaluated with confusion matrices,
accuracy, precision, recall and F1-score. In summary, most of the results are very
impressive. Our deep learning approach produced up to 97.5% accuracy and 98%
F1-score on COVID-19 vs. non-COVID-19 (normal or diseases excluding COVID-
19) class, while in classical machine learning approaches, the SVM with pretrained
features produced 98.9% accuracy, and at least 98.2% precision, recall and
F1-score on COVID-19 vs. non-COVID-19 class. These disease detection models
can be deployed for practical usage in the near future.