Existing Methods and Emerging
                  Trends for Novel Coronavirus (COVID-19) Detection Using Residual Network (ResNet):
                  A Review on Deep Learning Analysis

Bhattacharjya, Upasana; Sarma, Kandarpa Kumar

doi:10.1201/9781003171829-7

Cited by 4 publications

(2 citation statements)

References 0 publications

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“…As per the literature, although efficient algorithms have been proposed in this area, there are certain limitations to these studies. As reported in our previous publication of COVID-19 review [70] there are two fold shortcomings, which includes,

improper database and

limited database. Due to the noisy, incomplete, and unmarked data, issues like redundancy, sparsity, and non-availability of values are encountered during the training of classification modules.…”

Section: Discussionmentioning

confidence: 99%

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database

Bhattacharjya

Sarma²,

Medhi³

et al. 2023

Biomedical Signal Processing and Control

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improper database and

Section: Discussionmentioning

confidence: 99%

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database

Bhattacharjya

Sarma²,

Medhi³

et al. 2023

Biomedical Signal Processing and Control

Self Cite

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“…ResNet is constructed by several back-to-back residual blocks connected to a final linear fully-connected layer. In this study, we used ResNet as the third comparison baseline since ResNet has been widely used in automated clinical diagnosis of various diseases [4,13,51,58].…”

Section: Baseline For Comparisonmentioning

confidence: 99%

Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search

Asadi¹,

Poursalim²,

Loni³

et al. 2023

Preprint

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This paper presents a novel machine learning framework for detecting Paroxysmal Atrial Fibrillation (PxAF), a pathological characteristic of Electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning process, the framework involves a Generative Adversarial Network (GAN) along with a Neural Architecture Search (NAS) in the data preparation and classifier optimization phases. The GAN is innovatively invoked to overcome the class imbalance of the training data by producing the synthetic ECG for PxAF class in a certified manner. The effect of the certified GAN is statistically validated. Instead of using a general-purpose classifier, the NAS automatically designs a highly accurate convolutional neural network architecture customized for the PxAF classification task. Experimental results show that the accuracy of the proposed framework exhibits a high value of 99% which not only enhances state-of-the-art by up to 5.1%, but also improves the classification performance of the two widely-accepted baseline methods, ResNet-18, and Auto-Sklearn, by 2.2% and 6.1%.

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Accurate detection of paroxysmal atrial fibrillation with certified-GAN and neural architecture search

Asadi

Poursalim

Loni

et al. 2023

Sci Rep

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This paper presents a novel machine learning framework for detecting PxAF, a pathological characteristic of electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning process, the framework involves a generative adversarial network (GAN) along with a neural architecture search (NAS) in the data preparation and classifier optimization phases. The GAN is innovatively invoked to overcome the class imbalance of the training data by producing the synthetic ECG for PxAF class in a certified manner. The effect of the certified GAN is statistically validated. Instead of using a general-purpose classifier, the NAS automatically designs a highly accurate convolutional neural network architecture customized for the PxAF classification task. Experimental results show that the accuracy of the proposed framework exhibits a high value of 99.0% which not only enhances state-of-the-art by up to 5.1%, but also improves the classification performance of the two widely-accepted baseline methods, ResNet-18, and Auto-Sklearn, by $$2.2\%$$ 2.2 % and $$6.1\%$$ 6.1 % .

show abstract

Existing Methods and Emerging Trends for Novel Coronavirus (COVID-19) Detection Using Residual Network (ResNet): A Review on Deep Learning Analysis

Cited by 4 publications

References 0 publications

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database

Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search

Accurate detection of paroxysmal atrial fibrillation with certified-GAN and neural architecture search

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