# Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images

arXiv: https://arxiv.org/abs/2105.08199 · HTML: https://mlsanity.com/paper/2105.08199/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 49 references that are in the map.
- First posted on arXiv 17 May 2021.

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

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [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)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Evolving Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1710.10741/) (2017)
- [Deep Randomized Neural Networks](https://mlsanity.com/paper/2002.12287/) (2020)
- [COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios](https://mlsanity.com/paper/2004.05835/) (2020)
- [Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Images](https://mlsanity.com/paper/2004.05717/) (2020)
- [A Novel CNN-LSTM-based Approach to Predict Urban Expansion](https://mlsanity.com/paper/2103.01695/) (2021)
- [DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images](https://mlsanity.com/paper/2004.04582/) (2020)
- [Randomly weighted CNNs for (music) audio classification](https://mlsanity.com/paper/1805.00237/) (2018)

## Built on by (most cited first, 9 of 9)

- [TAU: A Framework for Video-Based Traffic Analytics Leveraging Artificial Intelligence and Unmanned Aerial Systems](https://mlsanity.com/paper/2303.00337/) (2023)
- [Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach](https://mlsanity.com/paper/2203.07728/) (2022)
- [Unlocking the Potential of Medical Imaging with ChatGPT's Intelligent Diagnostics](https://mlsanity.com/paper/2305.07429/) (2023)
- [An Effective Weight Initialization Method for Deep Learning: Application to Satellite Image Classification](https://mlsanity.com/paper/2406.00348/) (2024)
- [Contactless Human Activity Recognition using Deep Learning with Flexible and Scalable Software Define Radio](https://mlsanity.com/paper/2304.09756/) (2023)
- [Interpretation of immunofluorescence slides by deep learning techniques: anti-nuclear antibodies case study](https://mlsanity.com/paper/2306.12432/) (2023)
- [Revolutionizing Disease Diagnosis: A Microservices-Based Architecture for Privacy-Preserving and Efficient IoT Data Analytics Using Federated Learning](https://mlsanity.com/paper/2308.14017/) (2023)
- [Early Detection of Red Palm Weevil Infestations using Deep Learning Classification of Acoustic Signals](https://mlsanity.com/paper/2308.15829/) (2023)
- [Enhancing Early Alzheimer Disease Detection through Big Data and Ensemble Few-Shot Learning](https://mlsanity.com/paper/2510.19282/) (2025)

## Abstract (arXiv)

By the start of 2020, the novel coronavirus disease (COVID-19) has been declared a worldwide pandemic. Because of the severity of this infectious disease, several kinds of research have focused on combatting its ongoing spread. One potential solution to detect COVID-19 is by analyzing the chest X-ray images using Deep Learning (DL) models. In this context, Convolutional Neural Networks (CNNs) are presented as efficient techniques for early diagnosis. In this study, we propose a novel randomly initialized CNN architecture for the recognition of COVID-19. This network consists of a set of different-sized hidden layers created from scratch. The performance of this network is evaluated through two public datasets, which are the COVIDx and the enhanced COVID-19 datasets. Both of these datasets consist of 3 different classes of images: COVID19, pneumonia, and normal chest X-ray images. The proposed CNN model yields encouraging results with 94% and 99% of accuracy for COVIDx and enhanced COVID-19 dataset, respectively.