Deep Learning-Assisted Efficient Staging of SARS-CoV-2 Lesions Using Lung CT Slices

Sukanya, S. Arockia; Krishnamurthy, Kamalanand

doi:10.1155/2022/9613902

Cited by 2 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… ECNN layer: an important building block used in neural networks. This is the most famous and direct use of levels for information leading to triggers [ 22 ]. ERNN layer: it is a recurrent neural network layer in the deep learning approach.…”

Section: System Methodologymentioning

confidence: 99%

“…ECNN layer: an important building block used in neural networks. This is the most famous and direct use of levels for information leading to triggers [ 22 ].…”

Section: System Methodologymentioning

confidence: 99%

“…Various existing and related studies proposed a computer-based Omicron diagnosis architecture for infection prediction; however, these studies are not accurate and lead to huge errors due to common symptoms of distinct chest diseases, including the coronavirus disease 2019 [ 21 ]. The target of the proposed research work is to design and train the prototype to be able to successfully process CT-scan images and extract the features more accurately by preventing unrefined detection and ensuring the quality prediction of every pixel of the CT-scan image while ensuring framework execution and enhancing its flexibility through autodidact learning from earlier experiences [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

“…Table 2 lists and delivers more precise outcomes than molded feature-based prototypes [ 5 , 9 , 17 , 26 ]. A deep RNN-CNN [ 17 , 18 , 22 , 27 ] framework, COVIDNet-CT [ 12 ], RestNet [ 27 ], ERNN [ 17 , 28 ], and CNN [ 3 ] were established to detect and predict Omicron infection from chest CT-scan images. In [ 1 ], the authors proposed a CNN and LSTM-based DL technique to diagnose COVID-19 instinctively through the X-ray image dataset.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Gupta

Srinivasulu

Hiran

et al. 2022

Interdisciplinary Perspectives on Infectious Diseases

View full text Add to dashboard Cite

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.

show abstract

Section: System Methodologymentioning

confidence: 99%

“…ECNN layer: an important building block used in neural networks. This is the most famous and direct use of levels for information leading to triggers [ 22 ].…”

Section: System Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Gupta

Srinivasulu

Hiran

et al. 2022

Interdisciplinary Perspectives on Infectious Diseases

View full text Add to dashboard Cite

show abstract

“…[16] Respiratory secretions were analyzed using RT-PCR, which successfully confirmed the presence of viruses within 2 h. SARS-CoV-2 infection was suspected in all cases due to (1) common cold-like symptoms such as fever, cough, and dyspnea, or (2) proximity to a person infected with the closely related COVID-19. [17]…”

Section: Rt-pcrmentioning

confidence: 99%

A perspective on the effectiveness of SARS-Cov-2 rapid testing kits

Kaur,

Kumar Rangra

2023

Pharmaspire

View full text Add to dashboard Cite

Patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) are generally diagnosed by rapid antigen test (RAT) and reverse transcription-polymerase chain reaction (RT-PCR) tests. However, there is a conflict that the RT-PCR test is more effective than the RAT. The primary objective of this perspective is to compare and outline the effectiveness of SARS-CoV-2 rapid testing kits available in the market with RT-PCR tests. Recently published systematic reviews and meta-analysis reports containing comparative studies of RAT and RT-PCR were selected. The analysis results revealed that RAT kits provide remarkable specificity and sensitivity in the early stages of infection, particularly when the viral load is huge, compared to RT-PCR. In addition, a trustworthy substitute for nasopharyngeal sampling is the use of nasal specimens for antigen detection, which are patient-friendly and somewhat sensitive. RAT could be beneficial in the combat against the COVID-19 pandemic, but it has to be combined with appropriate monitoring of results that come back negative.

show abstract

Deep Learning-Assisted Efficient Staging of SARS-CoV-2 Lesions Using Lung CT Slices

Cited by 2 publications

References 34 publications

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

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

A perspective on the effectiveness of SARS-Cov-2 rapid testing kits

Contact Info

Product

Resources

About