Gray level co-occurrence matrix and extreme learning machine for Covid-19 diagnosis

Pi, Pengpeng; Lima, Dimas

doi:10.1016/j.ijcce.2021.05.001

Cited by 30 publications

(17 citation statements)

References 20 publications

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“…This proposed PSCNN model is compared with ten state-of-theart models: CSSNet (4), SMO (5), COVNet (6), WSF (7), WEBBO (8), FSVC (9), SVM (10), PZM (11), GLCM-ELM (12), and Jaya (13). The implementation of all the state-of-the-art models is the same as in previous experiments.…”

Section: Comparison To State-of-the-art Modelsmentioning

confidence: 99%

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

Wang

Zhu

Zhang

2021

Front. Public Health

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Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system.Methods: First, the n-conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. Moreover, multiple-way data augmentation and Grad-CAM are employed to avoid overfitting and locating lung lesions.Results: The mean and standard variation values of the seven measures of our model were 95.28 ± 1.03 (sensitivity), 95.78 ± 0.87 (specificity), 95.76 ± 0.86 (precision), 95.53 ± 0.83 (accuracy), 95.52 ± 0.83 (F1 score), 91.7 ± 1.65 (MCC), and 95.52 ± 0.83 (FMI).Conclusion: Our PSCNN is better than 10 state-of-the-art models. Further, we validate the optimal hyperparameters in our model and demonstrate the effectiveness of PatchShuffle.

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Section: Comparison To State-of-the-art Modelsmentioning

confidence: 99%

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

Wang

Zhu

Zhang

2021

Front. Public Health

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“…Using karush-kuhn tucker (KKT) optimality conditions, the least squares problem can be solved by Eq. (31) [27].…”

Section: Extreme Learning Machinementioning

confidence: 99%

Adaptive Controller of Bidirectional DC-DC Converter Based on Extreme Learning Machine for Electric Vehicle

2022

IJIES

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This paper proposed a novel adaptive controller of a non-isolated bidirectional coupled inductor DC converter based on an extreme learning machine (ELM) for electric vehicles. MATLAB and SIMULINK are used as the software platform to simulate the proposed method. Different operating conditions are considered to show the efficacy of the extreme learning machine. From the simulation results, it is found that by designing the controller based on an extreme learning machine the controller can automatically adjust the value depending on the operating conditions (motor moving forward, backward, and regenerative braking). In addition, the performance of the motor with DC converter based on ELM is compared with DC converter based on grey wolf optimization and based on conventional PI controller. This is indicated by the overshoot and the settling time is much better when using controller based on an extreme learning machine (overshoot 221.6 and settling time 3.8 second).

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“…In this study, the gray-level co-occurrence matrix is used to extract features from CT images of COVID-19 cases [27]. We use genetic algorithms to find optimal solutions, and cross-validation is used to test the performance of the algorithms [28].…”

Section: Introductionmentioning

confidence: 99%

Covid-19 Diagnosis by Gray-level Cooccurrence Matrix and Genetic Algorithm

Jiang

Brown

et al. 2022

EAI Endorsed Trans e-Learn

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Currently, improving the identification of COVID-19 with the help of computer vision and artificial intelligence has received great attention from researchers. This paper proposes a novel method for automatic detection of COVID-19 based on chest CT to help radiologists improve the speed and reliability of tests for diagnosing COVID-19. Our algorithm is a hybrid approach based on the Gray-level Cooccurrence Matrix and Genetic Algorithm. The Gray-level Cooccurrence Matrix (GLCM) was used to extract CT scan image features, GA algorithm was used as an optimizer, and a feedforward neural network was used as a classifier. Finally, we use 296 chest CT scan images to evaluate the detection performance of our proposed method. To more accurately evaluate the accuracy of the algorithm, 10-run 10-fold cross-validation was introduced. Experimental results show that our proposed method outperforms state-of-the-art methods in terms of Sensitivity, Accuracy, F1, MCC, and FMI.

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Gray level co-occurrence matrix and extreme learning machine for Covid-19 diagnosis

Cited by 30 publications

References 20 publications

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

RETRACTED: PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

Adaptive Controller of Bidirectional DC-DC Converter Based on Extreme Learning Machine for Electric Vehicle

Covid-19 Diagnosis by Gray-level Cooccurrence Matrix and Genetic Algorithm

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