# Evolving Deep Convolutional Neural Network by Hybrid Sine-Cosine and Extreme Learning Machine for Real-time COVID19 Diagnosis from X-Ray Images

arXiv: https://arxiv.org/abs/2105.14192 · HTML: https://mlsanity.com/paper/2105.14192/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 97 references that are in the map.
- First posted on arXiv 14 May 2021.

## Builds on (most cited first, 14 of 14)

- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [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)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (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)
- [COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images](https://mlsanity.com/paper/2003.11055/) (2020)
- [Automatically designing CNN architectures using genetic algorithm for image classification](https://mlsanity.com/paper/1808.03818/) (2018)
- [Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection](https://mlsanity.com/paper/2003.12338/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach](https://mlsanity.com/paper/2004.10641/) (2020)
- [Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines](https://mlsanity.com/paper/1711.05862/) (2017)
- [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)
- [A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC](https://mlsanity.com/paper/1903.01648/) (2019)
- [Resolution analysis in a lens-free on-chip digital holographic microscope](https://mlsanity.com/paper/1906.06231/) (2019)

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

- [MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification](https://mlsanity.com/paper/2401.00728/) (2024)

## Abstract (arXiv)

The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is perhaps the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters' stochastic tuning of ELM's supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine-cosine algorithm was utilized to tune the ELM's parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network's training time is only 0.9421 milliseconds and the overall detection test time for 3100 images is 2.721 seconds.