# Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images

arXiv: https://arxiv.org/abs/2104.01617 · HTML: https://mlsanity.com/paper/2104.01617/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2021.

## Built on by (most cited first, 1 of 1)

- [Multi-Feature Vision Transformer via Self-Supervised Representation Learning for Improvement of COVID-19 Diagnosis](https://mlsanity.com/paper/2208.01843/) (2022)

## Abstract (arXiv)

Computed tomography (CT) and chest X-ray (CXR) have been the two dominant imaging modalities deployed for improved management of Coronavirus disease 2019 (COVID-19). Due to faster imaging, less radiation exposure, and being cost-effective CXR is preferred over CT. However, the interpretation of CXR images, compared to CT, is more challenging due to low image resolution and COVID-19 image features being similar to regular pneumonia. Computer-aided diagnosis via deep learning has been investigated to help mitigate these problems and help clinicians during the decision-making process. The requirement for a large amount of labeled data is one of the major problems of deep learning methods when deployed in the medical domain. To provide a solution to this, in this work, we propose a semi-supervised learning (SSL) approach using minimal data for training. We integrate local-phase CXR image features into a multi-feature convolutional neural network architecture where the training of SSL method is obtained with a teacher/student paradigm. Quantitative evaluation is performed on 8,851 normal (healthy), 6,045 pneumonia, and 3,795 COVID-19 CXR scans. By only using 7.06% labeled and 16.48% unlabeled data for training, 5.53% for validation, our method achieves 93.61% mean accuracy on a large-scale (70.93%) test data. We provide comparison results against fully supervised and SSL methods. Code: https://github.com/endiqq/Multi-Feature-Semi-Supervised-Learning-for-COVID-19-CXR-Images