# COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray images

arXiv: https://arxiv.org/abs/2104.07279 · HTML: https://mlsanity.com/paper/2104.07279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [COVID-19 Image Data Collection: Prospective Predictions Are the Future](https://mlsanity.com/paper/2006.11988/) (2020)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images](https://mlsanity.com/paper/2003.11055/) (2020)
- [Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection](https://mlsanity.com/paper/2003.12338/) (2020)
- [Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms](https://mlsanity.com/paper/2006.12229/) (2020)

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

- [A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks](https://mlsanity.com/paper/2207.02160/) (2022)

## Abstract (arXiv)

The new Coronavirus is spreading rapidly, and it has taken the lives of many people so far. The virus has destructive effects on the human lung, and early detection is very important. Deep Convolution neural networks are such powerful tools in classifying images. Therefore, in this paper, a hybrid approach based on a deep network is presented. Feature vectors were extracted by applying a deep convolution neural network on the images, and useful features were selected by the binary differential meta-heuristic algorithm. These optimized features were given to the SVM classifier. A database consisting of three categories of images such as COVID-19, pneumonia, and healthy included in 1092 X-ray samples was considered. The proposed method achieved an accuracy of 99.43%, a sensitivity of 99.16%, and a specificity of 99.57%. Our results demonstrate that the suggested approach is better than recent studies on COVID-19 detection with X-ray images.