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.

翻译：COVID-19感染患者可能出现类似肺炎的症状以及可能损害肺部的呼吸系统问题。通过医学影像，可使用多种机器学习方法准确识别和预测冠状病毒疾病。多数已发表的机器学习方法可能需要大量的超参数调整，且不适用于小规模数据集。小样本学习算法通过利用相对较小数据集中的信息，旨在减少对大规模数据集的需求。这启发我们开发了一种用于早期检测COVID-19的小样本学习模型，以减轻这种危险疾病的后续影响。所提出的架构结合了小样本学习与集成预训练卷积神经网络，从CT扫描影像中提取特征向量进行相似性学习。所提出的三元组孪生网络作为小样本学习模型，将CT扫描影像分类为正常、COVID-19和社区获得性肺炎。该模型在每类仅使用200张CT扫描影像作为训练数据的情况下，实现了98.719%的整体准确率、99.36%的特异性、98.72%的敏感性和99.9%的ROC评分。