CODENET: A deep learning model for COVID-19 detection

Ju, Hong; Cui, Yanyan; Su, Qiaosen; Juan, Liran; Manavalan, Balachandran

doi:10.1016/j.compbiomed.2024.108229

Cited by 4 publications

(1 citation statement)

References 57 publications

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“…Applying the experimental results to the 30% testing data (i.e., 2766 images out of 9220) demonstrates the superiority of the hybrid DTL approach when coupled with NN, achieving the highest MCC of 0.814, followed by SVM with a linear kernel, which yielded an MCC of 0.805 when compared to existing pre-trained models of densely connected classifiers in the binary classification task. Others have proposed deep learning approaches to detect COVID-19 using X-ray and CT images [9][10][11][12][13][14]. Table 1 provides an overview of the existing works compared to our proposed work.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

Turki,

Al Habib,

Taguchi

2024

Mathematics

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Transmission electron microscopy imaging provides a unique opportunity to inspect the detailed structure of infected lung cells with SARS-CoV-2. Unlike previous studies, this novel study aims to investigate COVID-19 classification at the lung cellular level in response to SARS-CoV-2. Particularly, differentiating between healthy and infected human alveolar type II (hAT2) cells with SARS-CoV-2. Hence, we explore the feasibility of deep transfer learning (DTL) and introduce a highly accurate approach that works as follows: First, we downloaded and processed 286 images pertaining to healthy and infected hAT2 cells obtained from the electron microscopy public image archive. Second, we provided processed images to two DTL computations to induce ten DTL models. The first DTL computation employs five pre-trained models (including DenseNet201 and ResNet152V2) trained on more than one million images from the ImageNet database to extract features from hAT2 images. Then, it flattens and provides the output feature vectors to a trained, densely connected classifier with the Adam optimizer. The second DTL computation works in a similar manner, with a minor difference in that we freeze the first layers for feature extraction in pre-trained models while unfreezing and jointly training the next layers. The results using five-fold cross-validation demonstrated that TFeDenseNet201 is 12.37× faster and superior, yielding the highest average ACC of 0.993 (F1 of 0.992 and MCC of 0.986) with statistical significance ( from a t-test) compared to an average ACC of 0.937 (F1 of 0.938 and MCC of 0.877) for the counterpart (TFtDenseNet201), showing no significance results ( from a t-test).

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Section: Introduction and Related Workmentioning

confidence: 99%

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

Turki,

Al Habib,

Taguchi

2024

Mathematics

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

Nurjahan,

Mahbub-Or-Rashid,

Satu

et al. 2024

Iran J Comput Sci

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Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through Deep Transfer Learning Approach

Turki,

Al Habib,

Taguchi

2024

Preprint

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SARS-CoV-2 can infect alveoli, inducing a lung injury and thereby impairing the lung function. Healthy alveolar type II (AT2) cells play a major role in lung injury repair as well as keeping alveoli space free from fluids, which is not the case for infected AT2 cells. Unlike previous studies, this novel study aims to automatically differentiate between healthy and infected AT2 cells with SARS-CoV-2 through using efficient AI-based models, which can aid in disease control and treatment. Therefore, we introduce a highly accurate deep transfer learning (DTL) approach that works as follows. First, we downloaded and processed 286 images pertaining to healthy and infected human AT2 (hAT2) cells, obtained from the electron microscopy public image archive. Second, we provided processed images to two DTL computations to induce ten DTL models. The first DTL computation employs five pre-trained models (including DenseNet201 and ResNet152V2) trained on more than million images from ImageNet database to extract features from hAT2 images. Then, flattening and providing the output feature vectors to a trained densely connected classifier with Adam optimizer. The second DTL computation works in a similar manner with a minor difference in which we freeze the first layers for feature extraction in pre-trained models while unfreezing and training the next layers. Compared to TFtDenseNet201, experimental results using five-fold cross-validation demonstrate that TFeDenseNet201 is 12.37 × faster and superior, yielding the highest average ACC of 0.993 (F1 of 0.992 and MCC of 0.986) with statistical significance (P<2.2 × 10^(-16) from a t-test).

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CODENET: A deep learning model for COVID-19 detection

Cited by 4 publications

References 57 publications

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through the Deep Transfer Learning Approach

Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

Novel Automatic Classification of Human Adult Lung Alveolar Type II Cells Infected with SARS-CoV-2 through Deep Transfer Learning Approach

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