# COVID-19 Detection through Deep Feature Extraction

arXiv: https://arxiv.org/abs/2111.10762 · HTML: https://mlsanity.com/paper/2111.10762/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 14 references that are in the map.
- First posted on arXiv 21 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images](https://mlsanity.com/paper/2003.11055/) (2020)
- [COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs](https://mlsanity.com/paper/2003.14395/) (2020)
- [A Light CNN for detecting COVID-19 from CT scans of the chest](https://mlsanity.com/paper/2004.12837/) (2020)
- [Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning](https://mlsanity.com/paper/2004.12592/) (2020)

## Abstract (arXiv)

The SARS-CoV2 virus has caused a lot of tribulation to the human population. Predictive modeling that can accurately determine whether a person is infected with COVID-19 is imperative. The study proposes a novel approach that utilizes deep feature extraction technique, pre-trained ResNet50 acting as the backbone of the network, combined with Logistic Regression as the head model. The proposed model has been trained on Kaggle COVID-19 Radiography Dataset. The proposed model achieves a cross-validation accuracy of 100% on the COVID-19 and Normal X-Ray image classes. Similarly, when tested on combined three classes, the proposed model achieves 98.84% accuracy.