Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images

COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and −ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.

[1]  Muhammad Waleed Khan,et al.  A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron , 2022, Journal of Experimental & Theoretical Artificial Intelligence.

[2]  Fehaid Alqahtani,et al.  Hybrid Deep Learning Algorithm for Forecasting SARS-CoV-2 Daily Infections and Death Cases , 2022, Axioms.

[3]  Daren Zhao,et al.  Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models , 2022, Scientific Reports.

[4]  T. Anitha,et al.  Multiclass Classification for Detection of COVID-19 Infection in Chest X-Rays Using CNN , 2022, Computational intelligence and neuroscience.

[5]  A. Srinivasulu,et al.  Omicron Virus Data Analytics Using Extended RNN Technique , 2022, International Journal of Cancer Research & Therapy.

[6]  H. Miyoshi,et al.  High-resolution melting analysis to discriminate between the SARS-CoV-2 Omicron variants BA.1 and BA.2 , 2022, Biochemistry and Biophysics Reports.

[7]  A. Srinivasulu,et al.  Deep Learning based Detection and Prediction of Omicron Diagnosis on Collected Symptoms , 2022, 2022 7th International Conference on Communication and Electronics Systems (ICCES).

[8]  Arash Heidari,et al.  Machine learning applications for COVID-19 outbreak management , 2022, Neural Computing and Applications.

[9]  T. Toharudin,et al.  Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM , 2022, Applied Sciences.

[10]  Tao Jiang,et al.  Convolutional Neural Networks Based on Sequential Spike Predict the High Human Adaptation of SARS-CoV-2 Omicron Variants , 2022, Viruses.

[11]  Karrar Hameed Abdulkareem,et al.  A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation , 2022, Journal of personalized medicine.

[12]  N. Ibrahim,et al.  Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery , 2022, Journal of Infection and Public Health.

[13]  J. Bhiman,et al.  Early assessment of the clinical severity of the SARS-CoV-2 Omicron variant in South Africa , 2021, medRxiv.

[14]  K. To,et al.  SARS-CoV-2 Omicron variant shows less efficient replication and fusion activity when compared with Delta variant in TMPRSS2-expressed cells , 2021, Emerging microbes & infections.

[15]  M. Alhaisoni,et al.  COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion , 2021, Sensors.

[16]  Mohammad Al Rawajbeh,et al.  Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction , 2021, Frontiers in Public Health.

[17]  Ahmed M. El-Sherbeeny,et al.  A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection , 2021, Applied Sciences.

[18]  S. Madhi,et al.  The continuous evolution of SARS-CoV-2 in South Africa: a new lineage with rapid accumulation of mutations of concern and global detection , 2021, medRxiv.

[19]  Christine S. Smedley,et al.  Risk factors for COVID-19-related in-hospital mortality in a high HIV and tuberculosis prevalence setting in South Africa: a cohort study , 2021, The Lancet HIV.

[20]  Zuhaira M. Zain,et al.  COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach , 2021, J. Control. Sci. Eng..

[21]  M. Z. Islam,et al.  A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images , 2020, Informatics in Medicine Unlocked.

[22]  Ruben Morales-Menendez,et al.  Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet , 2020, Chaos, Solitons & Fractals.

[23]  Zhongke Feng,et al.  Prediction of the COVID-19 epidemic trends based on SEIR and AI models , 2020, medRxiv.

[24]  Yan Liu,et al.  Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV , 2020, Nature Communications.

[25]  M. Torremorell,et al.  Antigenic characterization of novel H1 influenza A viruses in swine , 2020, Scientific Reports.

[26]  Kai Zhao,et al.  A pneumonia outbreak associated with a new coronavirus of probable bat origin , 2020, Nature.

[27]  Investigation of novel SARS-COV-2 variant Variant of Concern 202012/01 , 2020 .

[28]  A. Walls,et al.  Structures of MERS-CoV spike glycoprotein in complex with sialoside attachment receptors , 2019, Nature Structural & Molecular Biology.

[29]  W. Su,et al.  Susceptibility of influenza A(H1N1)/pdm2009, seasonal A(H3N2) and B viruses to Oseltamivir in Guangdong, China between 2009 and 2014 , 2017, Scientific Reports.

[30]  S. Madhi,et al.  Severe Influenza-associated Respiratory Infection in High HIV Prevalence Setting, South Africa, 2009–2011 , 2013, Emerging infectious diseases.

[31]  H. Feldmann,et al.  Inhibition of novel β coronavirus replication by a combination of interferon-α2b and ribavirin , 2013, Scientific Reports.

[32]  Taeg S. Kim,et al.  Regulating the adaptive immune response to respiratory virus infection , 2012, Nature Reviews Immunology.

[33]  Anthony J McMichael,et al.  Social and environmental risk factors in the emergence of infectious diseases , 2004, Nature Medicine.