RETRACTED: Deep Fractional Max Pooling Neural Network for COVID-19 Recognition

Wang, Shuihua; Satapathy, Suresh Chandra; Anderson, D; Chen, Shi-Xin; Zhang, Yudong

doi:10.3389/fpubh.2021.726144

Cited by 18 publications

(7 citation statements)

References 31 publications

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“…COVID-19 directly endangers people's lives, and it is extremely important to diagnose COVID-19 quickly and accurately. The latest method proposed by Wang et al ( 64 , 65 ) may help diagnose COVID-19 more quickly and effectively. In the fight against COVID-19, when the psychological symptoms of medical workers are discovered and intervened in time, the work efficiency of the entire health system will be improved.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning

Sun

Zhang

et al. 2021

Front. Public Health

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Mental health prediction is one of the most essential parts of reducing the probability of serious mental illness. Meanwhile, mental health prediction can provide a theoretical basis for public health department to work out psychological intervention plans for medical workers. The purpose of this paper is to predict mental health of medical workers based on machine learning by 32 factors. We collected the 32 factors of 5,108 Chinese medical workers through questionnaire survey, and the results of Self-reporting Inventory was applied to characterize mental health. In this study, we propose a novel prediction model based on optimization algorithm and neural network, which can select and rank the most important factors that affect mental health of medical workers. Besides, we use stepwise logistic regression, binary bat algorithm, hybrid improved dragonfly algorithm and the proposed prediction model to predict mental health of medical workers. The results show that the prediction accuracy of the proposed model is 92.55%, which is better than the existing algorithms. This method can be used to predict mental health of global medical worker. In addition, the method proposed in this paper can also play a role in the appropriate work plan for medical worker.

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Section: Discussionmentioning

confidence: 99%

Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning

Sun

Zhang

et al. 2021

Front. Public Health

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“…FMP works better in a random way [ 83 ]. Wang et al [ 84 ] used fractional max pooling (FMP) instead of max pooling and average pooling in a network for COVID-19 recognition.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Survey on Deep Learning in COVID-19 Diagnosis

Han

Wang

et al. 2022

J. Imaging

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According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

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“…Considering that traditional methods are prone with low solution accuracy or system collapse due to lagging error when addressing the time-dependent problem [5]- [7]. To overcome these shortcomings, the derivative information in the TDARE problem is fully utilized in the ZNN model to effectively overcome the lagging error and improve the solution accuracy [8]- [10]. The ZNN model can achieve high accuracy in the aim problem, but noise interference can lead to collapse.…”

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confidence: 99%

“…The NACZNN model ( 3) is compared with other competitive methods including OZNN [8], NCZNN [16], and PTCZNN [12] models. For comparison, the adaptive scale coefficient for realizing the NACZNN model ( 3) is designed as .…”

mentioning

confidence: 99%

“…It can be found that the NACZNN model (3) achieves the highest solution accuracy, which reaches order and has no significant increase in convergence time. Unfortunately, OZNN [8], NCZNN [16], and PTCZNN [12] models are shown to converge to only order . Because NACZNN model (3) employed the norm-based adaptive coefficient , there is no need to repeatedly adjust the convergence coefficients to achieve the desired experimental results, as with other models.…”

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confidence: 99%

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