# Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms

arXiv: https://arxiv.org/abs/2006.12229 · HTML: https://mlsanity.com/paper/2006.12229/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 14 of 14)

- [Self-supervised deep convolutional neural network for chest X-ray classification](https://mlsanity.com/paper/2103.03055/) (2021)
- [UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection](https://mlsanity.com/paper/2105.08590/) (2021)
- [Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images](https://mlsanity.com/paper/2210.16584/) (2022)
- [Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors](https://mlsanity.com/paper/2208.10320/) (2022)
- [An Evaluation of Lightweight Deep Learning Techniques in Medical Imaging for High Precision COVID-19 Diagnostics](https://mlsanity.com/paper/2305.19016/) (2023)
- [COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray images](https://mlsanity.com/paper/2104.07279/) (2021)
- [Evolving Deep Convolutional Neural Network by Hybrid Sine-Cosine and Extreme Learning Machine for Real-time COVID19 Diagnosis from X-Ray Images](https://mlsanity.com/paper/2105.14192/) (2021)
- [A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging](https://mlsanity.com/paper/2108.04344/) (2021)
- [Pulmonary Disease Classification Using Globally Correlated Maximum Likelihood: an Auxiliary Attention mechanism for Convolutional Neural Networks](https://mlsanity.com/paper/2109.00573/) (2021)
- [WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep Imaging Ultrasound](https://mlsanity.com/paper/2311.10857/) (2023)
- [A Data Augmented Approach to Transfer Learning for Covid-19 Detection](https://mlsanity.com/paper/2108.02870/) (2021)
- [Robust and Interpretable COVID-19 Diagnosis on Chest X-ray Images using Adversarial Training](https://mlsanity.com/paper/2311.14227/) (2023)
- [The Impact of an XAI-Augmented Approach on Binary Classification with Scarce Data](https://mlsanity.com/paper/2407.06206/) (2024)
- [From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19](https://mlsanity.com/paper/2410.12589/) (2024)

## Abstract (arXiv)

As the rapid spread of coronavirus disease (COVID-19) worldwide, chest X-ray radiography has also been used to detect COVID-19 infected pneumonia and assess its severity or monitor its prognosis in the hospitals due to its low cost, low radiation dose, and wide accessibility. However, how to more accurately and efficiently detect COVID-19 infected pneumonia and distinguish it from other community-acquired pneumonia remains a challenge. In order to address this challenge, we in this study develop and test a new computer-aided diagnosis (CAD) scheme. It includes several image pre-processing algorithms to remove diaphragms, normalize image contrast-to-noise ratio, and generate three input images, then links to a transfer learning based convolutional neural network (a VGG16 based CNN model) to classify chest X-ray images into three classes of COVID-19 infected pneumonia, other community-acquired pneumonia and normal (non-pneumonia) cases. To this purpose, a publicly available dataset of 8,474 chest X-ray images is used, which includes 415 confirmed COVID-19 infected pneumonia, 5,179 community-acquired pneumonia, and 2,880 non-pneumonia cases. The dataset is divided into two subsets with 90% and 10% of images in each subset to train and test the CNN-based CAD scheme. The testing results achieve 94.0% of overall accuracy in classifying three classes and 98.6% accuracy in detecting Covid-19 infected cases. Thus, the study demonstrates the feasibility of developing a CAD scheme of chest X-ray images and providing radiologists useful decision-making supporting tools in detecting and diagnosis of COVID-19 infected pneumonia.