Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization

Wang, Shuihua; Wu, Xiaofeng; Zhang, Yudong; Tang, Chaosheng; Zhang, Xin

doi:10.2991/ijcis.d.200828.001

Cited by 74 publications

(48 citation statements)

References 27 publications

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“…We compared our proposed DFMPNN method with 10 state-of-the-art methods: WEBBO ( 8 ), 3SBBO ( 9 ), DeCovNet ( 10 ), FSVC ( 11 ), GN-COD ( 12 ), GLCMSVM ( 13 ), 5L-DCNN ( 14 ), CSSN ( 15 ), FCONet ( 16 ), COVNet ( 17 ). All the comparison was carried on over the same test set of 10 runs.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

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

Wang

Satapathy

Anderson

et al. 2021

Front. Public Health

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Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed “deep fractional max pooling neural network (DFMPNN)” to diagnose COVID-19 more efficiently.Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called “fractional max-pooling” (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness.Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%.Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).

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Section: Experiments Results and Discussionmentioning

confidence: 99%

“…Yao ( 8 ) proposed a wavelet entropy biogeography-based optimization (WEBBO) method for COVID-19 diagnosis. Wu ( 9 ) presented three-segment biogeography-based optimization (3SBBO) for recognizing COVID-19 patients. Wang et al ( 10 ) presented a DeCovNet.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Satapathy

Anderson

et al. 2021

Front. Public Health

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“…The features learned in the shallower layer are more general, and the features learned in the deeper layer are more relevant to specific tasks. In Figure 3 , the shallowest common feature “lines” are the same for the classification task of faces, cars, elephants, and chairs [ 36 ]. ImageNet 1000 classification is a subset of ImageNet, with a training set of about 1.2 million pieces, a verification set of 50,000 pieces, and a test set of 100,000 pieces.…”

Section: Methodsmentioning

confidence: 99%

Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning

Wang

Feng

Song

et al. 2021

Computational and Mathematical Methods in Medicine

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With the continuous improvement of human living standards, dietary habits are constantly changing, which brings various bowel problems. Among them, the morbidity and mortality rates of colorectal cancer have maintained a significant upward trend. In recent years, the application of deep learning in the medical field has become increasingly spread aboard and deep. In a colonoscopy, Artificial Intelligence based on deep learning is mainly used to assist in the detection of colorectal polyps and the classification of colorectal lesions. But when it comes to classification, it can lead to confusion between polyps and other diseases. In order to accurately diagnose various diseases in the intestines and improve the classification accuracy of polyps, this work proposes a multiclassification method for medical colonoscopy images based on deep learning, which mainly classifies the four conditions of polyps, inflammation, tumor, and normal. In view of the relatively small number of data sets, the network firstly trained by transfer learning on ImageNet was used as the pretraining model, and the prior knowledge learned from the source domain learning task was applied to the classification task about intestinal illnesses. Then, we fine-tune the model to make it more suitable for the task of intestinal classification by our data sets. Finally, the model is applied to the multiclassification of medical colonoscopy images. Experimental results show that the method in this work can significantly improve the recognition rate of polyps while ensuring the classification accuracy of other categories, so as to assist the doctor in the diagnosis of surgical resection.

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“…Deep learning has been successfully applied in medical imaging [7][8][9]. The major areas where medical imaging is applied are in the detection of skin cancer [10], brain tumors [11], stomach cancer [12], lung cancer [13], breast cancer [14], blood cancer [15], and COVID-19 [16,17]. Several techniques have been introduced by computer vision (CV) researchers for early coronavirus recognition using chest X-ray [18] and computed tomography (CT) [19] images, and AI and deep learning models have been applied for accurate classification [20,21].…”

Section: Introductionmentioning

confidence: 99%

COVID19 Classification Using CT Images via Ensembles of Deep Learning Models

Majid¹,

Khan²,

Nam³

et al. 2021

Computers, Materials &Amp; Continua

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The recent COVID-19 pandemic caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a significant impact on human life and the economy around the world. A reverse transcription polymerase chain reaction (RT-PCR) test is used to screen for this disease, but its low sensitivity means that it is not sufficient for early detection and treatment. As RT-PCR is a time-consuming procedure, there is interest in the introduction of automated techniques for diagnosis. Deep learning has a key role to play in the field of medical imaging. The most important issue in this area is the choice of key features. Here, we propose a set of deep learning features based on a system for automated classification of computed tomography (CT) images to identify COVID-19. Initially, this method was used to prepare a database of three classes: Pneumonia, COVID-19, and Healthy. The dataset consisted of 6000 CT images refined by a hybrid contrast stretching approach. In the next step, two advanced deep learning models (ResNet50 and DarkNet53) were fine-tuned and trained through transfer learning. The features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization approach. For each deep model, the Rao-1 algorithm and the PSO algorithm were combined in the hybrid approach. Later, the selected features were merged using the new minimum parallel distance non-redundant (PMDNR) approach. The final fused vector was finally classified using the extreme machine classifier. The experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%. Comparing the different classification algorithms at the

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Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization

Cited by 74 publications

References 27 publications

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

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

Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning

COVID19 Classification Using CT Images via Ensembles of Deep Learning Models

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