Related Experiment Video
Updated: Nov 10, 2025
High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
COVID-19 vs influenza viruses: A cockroach optimized deep neural network classification approach
Mohamed A El-Dosuky1, Mona Soliman2, Aboul Ella Hassanien2
1Faculty of Computers and Info Mansoura University Mansoura Egypt.
Abstract:
Among Coronavirus, as with many other viruses, receptor interactions are an essential determinant of species specificity, virulence, and pathogenesis. The pathogenesis of the COVID-19 depends on the virus's ability to attach to and enter into a suitable human host cell. This paper presents a cockroach optimized deep neural network to detect COVID-19 and differentiate between COVID-19 and influenza types A, B, and C. The deep network architecture is inspired using a cockroach optimization algorithm to optimize the deep neural network hyper-parameters. COVID-19 sequences are obtained from repository 2019 Novel Coronavirus Resource, and influenza A, B, and C sub-dataset are obtained from other repositories. Five hundred ninety-four unique genomes sequences are used in the training and testing process with 99% overall accuracy for the classification model.
Insights
This study introduces a novel deep neural network, optimized using a cockroach algorithm, for accurate COVID-19 detection. The model effectively distinguishes COVID-19 from influenza A, B, and C with 99% accuracy.
Area of Science:
- Virology
- Computational Biology
- Artificial Intelligence
Background:
- Viral pathogenesis, including COVID-19, is heavily influenced by host cell receptor interactions.
- Understanding these interactions is crucial for diagnosing and differentiating viral infections.
- Accurate and rapid diagnostic tools are essential for managing outbreaks.
Purpose of the Study:
- To develop a deep neural network for detecting COVID-19.
- To differentiate COVID-19 from influenza types A, B, and C using genomic sequence data.
- To optimize the deep neural network architecture using a cockroach optimization algorithm.
Main Methods:
- Utilized genomic sequences of COVID-19 and influenza A, B, and C.
- Employed a deep neural network architecture inspired by a cockroach optimization algorithm.
- Trained and tested the model using 594 unique genome sequences.
Main Results:
- Achieved 99% overall accuracy in classifying viral sequences.
- Successfully differentiated between COVID-19 and influenza types A, B, and C.
- Demonstrated the efficacy of the cockroach optimization algorithm in enhancing deep network performance.
Conclusions:
- The proposed cockroach-optimized deep neural network is a highly accurate tool for COVID-19 detection and differentiation from influenza.
- This approach offers a promising computational method for viral diagnostics.
- Further research can explore this optimization technique for other viral pathogens.
