MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification

Li, Chengfan; Xu, Yi-Duo; Ding, Xuehai; Zhao, Junjuan; Du, Rui-Qi; Wu, Lizhong; Sun, Wenyang

doi:10.1016/j.compbiomed.2022.105340

Cited by 23 publications

(11 citation statements)

References 34 publications

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“…We compared the AMFRW with 9 state-of-the-art approaches including Inf-Net [34] , Chain code-SVM [35] , CNN-Clustering [36] , MLT [37] , GAN-Unet [38] , U-Net++ [39] , R2U-Net [40] , FCN [41] , and CPMC [42] to do segmentation simulations on the COVID-19 CT image of vascular adhesive GGO, and further comparing and analyzing the

and

under different segmentation methods. The comparative COVID-19 CT image segmentation simulations of vascular adhesive GGO are shown in Fig.…”

Section: Analysis Of Experimental Results and Performancementioning

confidence: 99%

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm

Wang

Guo

Han

et al. 2023

Biomedical Signal Processing and Control

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and

under different segmentation methods. The comparative COVID-19 CT image segmentation simulations of vascular adhesive GGO are shown in Fig.…”

Section: Analysis Of Experimental Results and Performancementioning

confidence: 99%

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm

Wang

Guo

Han

et al. 2023

Biomedical Signal Processing and Control

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“…Reference [27] proposed a multi-scale dilated convolutional network (MSDC-Net), which uses dilated convolution to capture contextual semantic information at different scales, and aggregates feature learned in multiple modules during downsampling to improve segmentation accuracy. Reference [28] proposed a multi-net model (MultiR-Net) that combines COVID-19 classification and lesion segmentation, fusing features between two sub-networks through a reverse attention mechanism and an iterative training strategy. Reference [29] proposed a deep neural network (DNN) framework with multi-dimensional kernels and dilated residual blocks in the encoding process to obtain variable receptive fields in feature extraction.…”

Section: B Covid-19 Image Segmentationmentioning

confidence: 99%

“…A semi-supervised segmentation system is introduced to alleviate the lack of data. Reference [28] propose a noise-resistant segmentation framework ((COPLE-Net)) to improve the performance of handling noisy labels so that two adaptive mechanisms are interrelated.…”

Section: B Covid-19 Image Segmentationmentioning

confidence: 99%

Loop Residual Attention Network for Automatic Segmentation of COVID-19 Chest X-Ray Images

Yue

Lin

et al. 2023

IEEE Access

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As COVID-19 continues to put pressure on the global healthcare industry, using artificial intelligence to analyze chest X-rays (CXR) has become an effective way to diagnose the virus and treat patients. Despite that many studies have made significant progress in COVID-19 detection, accurately segmenting infected regions with variable locations and scales from COVID-19 CXR remains challenging. Therefore, this paper proposes a novel framework for COVID-19 CXR image segmentation. Specifically, we design a loop residual module to cyclically extract feature information in the process of encoding and decoding splicing, avoiding the loss of complex semantic information in network computing. At the same time, an absolute position information coding block is proposed to strengthen the position information of feature pixels. Moreover, a hybrid attention module is designed to establish semantic associations between channels and multi-scale spaces. Better feature representation is formed by the fusion of location and scale information to alleviate the impact of variable infection regions on segmentation performance. Extensive experiments are conducted on the public COVID-19 CXR dataset COVID-Qu-Ex, and the results show that our network is leading and robust compared to other networks in COVID-19 segmentation.

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“…In our work, we explore the latent representations generated by VAE and leverage the capabilities of ensemble learning to design three ensembled variational autoencoder models to classify COVID-19. Several other methods and techniques to detect and classify COVID-19 have been proposed in the literature [ 15 , 17 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ]. Table 1 summarizes the related works.…”

Section: Related Workmentioning

confidence: 99%

EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images

et al. 2022

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The COVID-19 pandemic has had a significant impact on many lives and the economies of many countries since late December 2019. Early detection with high accuracy is essential to help break the chain of transmission. Several radiological methodologies, such as CT scan and chest X-ray, have been employed in diagnosing and monitoring COVID-19 disease. Still, these methodologies are time-consuming and require trial and error. Machine learning techniques are currently being applied by several studies to deal with COVID-19. This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease. Two encoders are trained on chest X-ray images to generate two feature maps. The feature maps are concatenated and passed to either a combined or individual reparameterization phase to generate latent embeddings by sampling from a distribution. The latent embeddings are concatenated and passed to a classification head for classification. The COVID-19 Radiography Dataset from Kaggle is the source of chest X-ray images. The performances of the three models are evaluated. The proposed model shows satisfactory performance, with the best model achieving 99.19% and 98.66% accuracy on four classes and three classes, respectively.

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MultiR-Net: A Novel Joint Learning Network for COVID-19 segmentation and classification

Cited by 23 publications

References 34 publications

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm

Loop Residual Attention Network for Automatic Segmentation of COVID-19 Chest X-Ray Images

EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images

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