COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach

TL;DR

This paper demonstrates that few-shot deep learning, especially a custom Siamese network, can effectively detect COVID-19 from limited chest X-ray data, achieving high accuracy with minimal data.

Contribution

The study introduces a novel few-shot learning approach using Siamese networks for COVID-19 detection from scarce chest X-ray images.

Findings

Achieved 96.4% accuracy with the proposed method.

Improved from 83% baseline accuracy.

Validated effectiveness of few-shot learning in medical imaging.

Abstract

In the current COVID-19 pandemic situation, there is an urgent need to screen infected patients quickly and accurately. Using deep learning models trained on chest X-ray images can become an efficient method for screening COVID-19 patients in these situations. Deep learning approaches are already widely used in the medical community. However, they require a large amount of data to be accurate. The open-source community collectively has made efforts to collect and annotate the data, but it is not enough to train an accurate deep learning model. Few-shot learning is a sub-field of machine learning that aims to learn the objective with less amount of data. In this work, we have experimented with well-known solutions for data scarcity in deep learning to detect COVID-19. These include data augmentation, transfer learning, and few-shot learning, and unsupervised learning. We have also proposed a custom few-shot learning approach to detect COVID-19 using siamese networks. Our experimental results showcased that we can implement an efficient and accurate deep learning model for COVID-19 detection by adopting the few-shot learning approaches even with less amount of data. Using our proposed approach we were able to achieve 96.4% accuracy an improvement from 83% using baseline models.