Attention-Based Residual Learning Network for COVID-19 Detection Using Chest CT Images

Karthik, R.; Menaka, R.; Anand, Sundar; Johnson, Annie; Srilakshmi, Kanagala

doi:10.1007/978-3-030-87019-5_21

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…In [15] proposes a multilayer boundary perception-self attention mechanism deep learning model of image segmentation algorithm R-UNET segmentation model, focusing on solving the boundary localization problem in model segmentation, while deep mining network structure to build global information features to reverse the accuracy of segmentation. In [16] major contribution is to embed the attention residual network into the deep learning framework structure to build the COVID-19 CT image detection model and prove its effectiveness. In [17] adopting deep learning network AlexNet as the backbone is used.…”

Section: Relate Workmentioning

confidence: 99%

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images

Sun

Feng²,

Liu³

et al. 2023

Biomedical Signal Processing and Control

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Section: Relate Workmentioning

confidence: 99%

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images

Sun

Feng²,

Liu³

et al. 2023

Biomedical Signal Processing and Control

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“…With the advancement of deep learning technology, many CC-COVID approaches have been proposed for rapid screening of COVID-19 [15] , [23] , [24] , [25] , [30] , [31] , [32] , [33] , [34] , which generally fall into two categories, i.e., 3D CNN-based methods and 2D CNN-based methods. The comprehensive view of some related methods for automated CC-COVID is described in Table 1 .…”

Section: Related Workmentioning

confidence: 99%

“…Karthik et al. [30] further proposed an attention-based residual learning block integrating with GoogLeNet, which obtained an accuracy of 91.26% with 349 chest CT images and 398 non-COVID CT images. However, the above CC-COVID methods only have the capability of identifying confirmed cases from abundant suspected cases but are unable to provide useful support for physicians in evaluating the severity and prognosis of COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Self-paced Multi-view Learning for CT-based severity assessment of COVID-19

Liu

Chen

Zhang

et al. 2023

Biomedical Signal Processing and Control

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SSLnDO-Based Deep Residual Network and RV-Coefficient Integrated Deep Fuzzy Clustering for Cotton Crop Classification

Sheela

Karthika

Janet

2023

Int. J. Info. Tech. Dec. Mak.

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The excellent classification accuracy of remote sensory images has led to a major expansion of their use in agricultural field surveillance in recent years. The accurate classification of crop is very significant for agricultural management and food security. There are diverse varieties of crops are available. Among them, each possesses same spectral curves. Due to increasing number of remote sensing information, a major limitation arises in the domain of crop classification is how to determine significant data from huge volume of information to maintain categorization accuracy and implementation time. Consequently, one of the main obstacles in the agricultural industry is the precise classification of crops. Deep Residual Network (ResNet)-based Social Sea Lion Driver Optimization (SSLnDO)-based Deep Fuzzy Clustering (DFC), where the distance is calculated using RV coefficient, are used to develop an effective method for classifying cotton crops. Finally, classification of the cotton crop is successfully accomplished using Deep ResNet, and the network classifier is trained by inserting produced SSLnDO. The vegetation index is generated for each color band independently. Combining the Social Ski Driver (SSD) optimization and the Sea Lion Optimization (SLnO) method yields the newly developed SSLnDO. A maximum testing accuracy of 0.940, a sensitivity of 0.931, and a specificity of 0.926 have been achieved by the designed model. When comparing the accuracy value produced by the proposed method for image-2 to the existing approaches, namely WLI-Fuzzy + BS-Lion NN, OSVM-OCNN, GAN, Hybrid CNN-RF, Deep learning, and Deep ResNet, they are 15.21%, 11.06%, 15.21%, 9.04%, 4.36%, and 2.3% higher.

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Attention-Based Residual Learning Network for COVID-19 Detection Using Chest CT Images

Cited by 3 publications

References 39 publications

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images

Self-paced Multi-view Learning for CT-based severity assessment of COVID-19

SSLnDO-Based Deep Residual Network and RV-Coefficient Integrated Deep Fuzzy Clustering for Cotton Crop Classification

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