  Patients with the COVID-19 infection may have pneumonia-like symptoms as well
as respiratory problems which may harm the lungs. From medical images,
coronavirus illness may be accurately identified and predicted using a variety
of machine learning methods. Most of the published machine learning methods may
need extensive hyperparameter adjustment and are unsuitable for small datasets.
By leveraging the data in a comparatively small dataset, few-shot learning
algorithms aim to reduce the requirement of large datasets. This inspired us to
develop a few-shot learning model for early detection of COVID-19 to reduce the
post-effect of this dangerous disease. The proposed architecture combines
few-shot learning with an ensemble of pre-trained convolutional neural networks
to extract feature vectors from CT scan images for similarity learning. The
proposed Triplet Siamese Network as the few-shot learning model classified CT
scan images into Normal, COVID-19, and Community-Acquired Pneumonia. The
suggested model achieved an overall accuracy of 98.719%, a specificity of
99.36%, a sensitivity of 98.72%, and a ROC score of 99.9% with only 200 CT
scans per category for training data.
