Gray level co-occurrence matrix and Schmitt neural  network for Covid-19 diagnosis

Pi, Pengpeng

doi:10.4108/eai.11-8-2021.170668

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…The SNELM model is compared with seven state-of-the-art COVID-19 recognition models over two datasets. The comparison models consist of FSV [ 3 ], 3SBBO [ 4 ], CNNSP [ 5 ], GCMSVM [ 6 ], WEJ [ 7 ], GCMSNN [ 8 ], SaPSO [ 9 ], DLA [ 10 ], DeCovNet [ 11 ], and DLM [ 12 ]. Particularly, CNNSP [ 5 ], DLA [ 10 ], DeCovNet [ 11 ], and DLM [ 12 ] are deep learning models.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

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SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

Zhang¹,

Khan²,

Zhu³

et al. 2023

Computer Systems Science and Engineering

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Aim) The COVID-19 has caused 6.26 million deaths and 522.06 million confirmed cases till 17/May/2022. Chest computed tomography is a precise way to help clinicians diagnose COVID-19 patients. (Method) Two datasets are chosen for this study. The multiple-way data augmentation, including speckle noise, random translation, scaling, salt-and-pepper noise, vertical shear, Gamma correction, rotation, Gaussian noise, and horizontal shear, is harnessed to increase the size of the training set. Then, the SqueezeNet (SN) with complex bypass is used to generate SN features. Finally, the extreme learning machine (ELM) is used to serve as the classifier due to its simplicity of usage, quick learning speed, and great generalization performances. The number of hidden neurons in ELM is set to 2000. Ten runs of 10-fold cross-validation are implemented to generate impartial results. (Result) For the 296-image dataset, our SNELM model attains a sensitivity of 96.35 ± 1.50%, a specificity of 96.08 ± 1.05%, a precision of 96.10 ± 1.00%, and an accuracy of 96.22 ± 0.94%. For the 640-image dataset, the SNELM attains a sensitivity of 96.00 ± 1.25%, a specificity of 96.28 ± 1.16%, a precision of 96.28 ± 1.13%, and an accuracy of 96.14 ± 0.96%. (Conclusion) The proposed SNELM model is successful in diagnosing COVID-19. The performances of our model are higher than seven state-of-the-art COVID-19 recognition models.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

“…Wang [ 7 ] proposed a wavelet entropy and Jaya (WEJ) algorithm. Pi [ 8 ] merged GCM with Schmitt neural network (SNN) for COVID-19 diagnosis. Their model is named GCMSNN.…”

Section: Introductionmentioning

confidence: 99%

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

Zhang¹,

Khan²,

Zhu³

et al. 2023

Computer Systems Science and Engineering

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“…This study compares the proposed ELUCNN model with SOTA COVID-19 diagnosis models on this entire 640-image dataset using ten runs of tenfold CV. The 14 comparison models comprise K-ELM (Yang 2018 ), CNN-SP (Zhang 2022a ), COVNet (Li et al 2020 ), DLA (Ni et al 2020 ), WSF (Wang et al 2020 ), DC-Net (Zhang 2022b ), WRE (Wu 2020 ), FSVC (El-kenawy et al 2020 ), GLCM (Chen 2020 ), 6l-DCNN (Hou 2022 ), PZM (Khan 2021 ), Jaya (Wang 2021 ), SNN (Pi 2021 ), and DLM (Gafoor et al 2022 ). Note here K-ELM (Yang 2018 ) is originally developed for brain detection.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Wang ( 2021 ) used the Jaya algorithm to classify COVID-19. Pi ( 2021 ) used Schmitt neural network (SCNN) to classify COVID-19. Gafoor et al ( 2022 ) developed a deep learning model (DLM) for detecting COVID-19 using Chest X-ray images.…”

Section: Introductionmentioning

confidence: 99%

ELUCNN for explainable COVID-19 diagnosis

et al. 2023

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COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client–server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of $$94.41\pm 0.98$$ 94.41 ± 0.98 , a specificity of $$94.84\pm 1.21$$ 94.84 ± 1.21 , an accuracy of $$94.62\pm 0.96$$ 94.62 ± 0.96 , and an F1 score of $$94.61\pm 0.95$$ 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.

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“…Chen (2021) used the effectiveness of the greyscale cooccurrence matrix (GLCM) for texture feature extraction to extract features from chest CT images and a support vector machine (SVM) to perform binary classification on these features to achieve the diagnosis of COVID-19. Pi and Lima (2021) and Pi (2021) also used GLCM as a feature extraction method. Among them, Pi and Lima (2021) used the extreme learning machine (ELM) as a classifier to classify the features extracted from chest CT by GLCM, and Pi (2021) used the Schmitt Neural Network as the classifier of the model.…”

Section: Introductionmentioning

confidence: 99%

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

Wang¹,

Pei²,

WANG³

et al. 2023

Biocell

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Since 2019, the coronavirus disease-19 (COVID-19) has been spreading rapidly worldwide, posing an unignorable threat to the global economy and human health. It is a disease caused by severe acute respiratory syndrome coronavirus 2, a single-stranded RNA virus of the genus Betacoronavirus. This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells. With the increase in the number of confirmed COVID-19 diagnoses, the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent. Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure. Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed. However, traditional hyperparameter tuning methods are usually time-consuming and unstable. To solve this issue, we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network (PSTCNN), allowing the model to tune hyperparameters automatically. Therefore, the proposed approach can reduce human involvement. Also, the optimisation algorithm can select the combination of hyperparameters in a targeted manner, thus stably achieving a solution closer to the global optimum. Experimentally, the PSTCNN can obtain quite excellent results, with a sensitivity of 93.65% ± 1.86%, a specificity of 94.32% ± 2.07%, a precision of 94.30% ± 2.04%, an accuracy of 93.99% ± 1.78%, an F1-score of 93.97% ± 1.78%, Matthews Correlation Coefficient of 87.99% ± 3.56%, and Fowlkes-Mallows Index of 93.97% ± 1.78%. Our experiments demonstrate that compared to traditional methods, hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

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Gray level co-occurrence matrix and Schmitt neural network for Covid-19 diagnosis

Cited by 6 publications

References 21 publications

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

SNELM: SqueezeNet-Guided ELM for COVID-19 Recognition

ELUCNN for explainable COVID-19 diagnosis

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

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