An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19

Pustokhin, Denis A.; Pustokhina, Irina V.; Dinh, Phuoc Nguyen; Phan, Son Van; Nguyen, Gia Nhu; Joshi, Gyanendra Prasad; Shankar, K.

doi:10.1080/02664763.2020.1849057

Cited by 41 publications

(24 citation statements)

References 27 publications

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“…For image analysis, two clinical datasets were considered from open Github archives [30][31][32]. This archive contains patients' CXR images of COVID-19 and 15 different illness types.…”

Section: Cxr Image Collectionmentioning

confidence: 99%

Deep Optimal VGG16 Based COVID-19 Diagnosis Model

Buvana¹,

Muthumayil²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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outbreak was first identified in Wuhan, China in December 2019. It was tagged as a pandemic soon by the WHO being a serious public medical condition worldwide. In spite of the fact that the virus can be diagnosed by qRT-PCR, COVID-19 patients who are affected with pneumonia and other severe complications can only be diagnosed with the help of Chest X-Ray (CXR) and Computed Tomography (CT) images. In this paper, the researchers propose to detect the presence of COVID-19 through images using Best deep learning model with various features. Impressive features like Speeded-Up Robust Features (SURF), Features from Accelerated Segment Test (FAST) and Scale-Invariant Feature Transform (SIFT) are used in the test images to detect the presence of virus. The optimal features are extracted from the images utilizing DeVGGCovNet (Deep optimal VGG16) model through optimal learning rate. This task is accomplished by exceptional mating conduct of Black Widow spiders. In this strategy, cannibalism is incorporated. During this phase, fitness outcomes are rejected and are not satisfied by the proposed model. The results acquired from real case analysis demonstrate the viability of DeVGGCovNet technique in settling true issues using obscure and testing spaces. VGG 16 model identifies the image which has a place with which it is dependent on the distinctions in images. The impact of the distinctions on labels during training stage is studied and predicted for test images. The proposed model was compared with existing state-of-the-art models and the results from the proposed model for disarray grid estimates like Sen, Spec, Accuracy and F1 score were promising.

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“…For image analysis, two clinical datasets were considered from open Github archives [30][31][32]. This archive contains patients' CXR images of COVID-19 and 15 different illness types.…”

Section: Cxr Image Collectionmentioning

confidence: 99%

Deep Optimal VGG16 Based COVID-19 Diagnosis Model

Buvana¹,

Muthumayil²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The COVID19 disease affects the human lungs, which can be identified by examining the lung X-ray. A successful CNN approach has been used with a convolutional neural network (CNN) to predict the Pneumonia case from chest X-ray [16][17][18][19][20]. The training process take place on a set of images offering the front view of lung X-ray images [21].…”

Section: Related Workmentioning

confidence: 99%

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

et al. 2021

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In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods.

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“…Table 3 and Fig. 8 provides a detailed results of the comparison made between FM-CNN model and the existing methods (Pustokhin et al 2020;Shankar and Perumal…”

Section: Performance Validationmentioning

confidence: 99%

“…Regardless, the researchers have addressed the maximum simulation outcome. In the literature (Pustokhin et al 2020), a novel IoT-enabled Depthwise separable Convolution Neural Network (DWS-CNN) with Deep Support Vector Machine (DSVM) was proposed for diagnosis and classification of COVID-19. A novel Residual Network (ResNet)-based Class Attention Layer with Bidirectional LSTM called RCAL-BiLSTM was proposed for COVID-19 detection (Shankar and Perumal 2020).…”

Section: Introductionmentioning

confidence: 99%

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

Shankar

Mohanty²,

Yadav

et al. 2021

Cogn Neurodyn

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was first identified in December 2019 at Wuhan, China. At present, the outbreak of COVID-19 pandemic has resulted in severe consequences on both economic and social infrastructures of the developed and developing countries. Several studies have been conducted and ongoing still to design efficient models for diagnosis and treatment of COVID-19 patients. The traditional diagnostic models that use reverse transcription-polymerase chain reaction (rt-qPCR) is a costly and time-consuming process. So, automated COVID-19 diagnosis using Deep Learning (DL) models becomes essential. The primary intention of this study is to design an effective model for diagnosis and classification of COVID-19. This research work introduces an automated COVID-19 diagnosis process using Convolutional Neural Network (CNN) with a fusion-based feature extraction model, called FM-CNN. FM-CNN model has three major phases namely, pre-processing, feature extraction, and classification. Initially, Wiener Filtering (WF)-based preprocessing is employed to discard the noise that exists in input chest X-Ray (CXR) images. Then, the pre-processed images undergo fusion-based feature extraction model which is a combination of Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRM), and Local Binary Patterns (LBP). In order to determine the optimal subset of features, Particle Swarm Optimization (PSO) algorithm is employed. At last, CNN is deployed as a classifier to identify the existence of binary and multiple classes of CXR images. In order to validate the proficiency of the proposed FM-CNN model in terms of its diagnostic performance, extension experimentation was carried out upon CXR dataset. As per the results attained from simulation, FM-CNN model classified multiple classes with the maximum sensitivity of 97.22%, specificity of 98.29%, accuracy of 98.06%, and F-measure of 97.93%.

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An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19

Cited by 41 publications

References 27 publications

Deep Optimal VGG16 Based COVID-19 Diagnosis Model

Deep Optimal VGG16 Based COVID-19 Diagnosis Model

Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

Automated COVID-19 diagnosis and classification using convolutional neural network with fusion based feature extraction model

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