# A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images

arXiv: https://arxiv.org/abs/2110.06340 · HTML: https://mlsanity.com/paper/2110.06340/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 71 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 16 of 16)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2004.04931/) (2020)
- [Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning](https://mlsanity.com/paper/2004.09363/) (2020)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images](https://mlsanity.com/paper/2003.11055/) (2020)
- [Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network](https://mlsanity.com/paper/2003.13815/) (2020)
- [COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images](https://mlsanity.com/paper/2009.05383/) (2020)
- [COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images](https://mlsanity.com/paper/2006.01409/) (2020)
- [Advance Warning Methodologies for COVID-19 using Chest X-Ray Images](https://mlsanity.com/paper/2006.05332/) (2020)
- [Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost](https://mlsanity.com/paper/2109.02428/) (2021)
- [Detection of Covid-19 From Chest X-ray Images Using Artificial Intelligence: An Early Review](https://mlsanity.com/paper/2004.05436/) (2020)

## Built on by (most cited first, 6 of 6)

- [Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM](https://mlsanity.com/paper/2203.14275/) (2022)
- [Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM](https://mlsanity.com/paper/2206.04548/) (2022)
- [Diagnosis of Parkinson's Disease Based on Voice Signals Using SHAP and Hard Voting Ensemble Method](https://mlsanity.com/paper/2210.01205/) (2022)
- [Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods](https://mlsanity.com/paper/2209.01380/) (2022)
- [PD-ADSV: An Automated Diagnosing System Using Voice Signals and Hard Voting Ensemble Method for Parkinson's Disease](https://mlsanity.com/paper/2304.06016/) (2023)
- [SyMPox: An Automated Monkeypox Detection System Based on Symptoms Using XGBoost](https://mlsanity.com/paper/2310.19801/) (2023)

## Abstract (arXiv)

The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia).