A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

Huang, Meiling; Liao, Yu-Chieh

doi:10.1016/j.compbiomed.2022.105604

Cited by 60 publications

(23 citation statements)

References 62 publications

(66 reference statements)

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“…More importantly, the “black box” [ [40] , [41] , [42] ] prevents DNNs from being plausible. For effective detection of COVID-19, Huang et al [ 43 ] proposed a lightweight network LightEfficientNetV2, by using fewer parameters to overcome data shortages and obtain higher performance. Kumar et al [ 44 ] combined graph convolutional network and convolutional neural network for determining the presence of COVID-19 infection in CXR images.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Liu

Cai

Tang

et al. 2022

Computers in Biology and Medicine

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Section: Introductionmentioning

confidence: 99%

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

Liu

Cai

Tang

et al. 2022

Computers in Biology and Medicine

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“…Huang et al [ 59 ], just as Aggarwal et al [ 56 ], employed seven pre-trained deep learning models in two scenarios. First finetuning without focusing on the reduction of the computational complexity of the models and secondly, he focused on the computational complexity of the models.…”

Section: Results Discussionmentioning

confidence: 99%

LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images

Ukwuoma¹,

Qin²,

Agbesi³

et al. 2022

Computers in Biology and Medicine

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“…Pre-trained models like Xception, InceptionV3, and EfficientNetV2 were used to identify COVID-19 in CXR and CT images. For the CXR dataset, EfficientNetV2 with fine tuning performed the best, but the LightEfficientNetV2 model performed the best for the CT data set [ 31 ]. In another study, a multi-classification model was proposed for four classes (normal, COVID-19, Pneumonia, and lung cancer) by combining CXR and CT images.…”

Section: Related Workmentioning

confidence: 99%

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization

2023

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Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don’t show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique’s addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset.

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A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

Cited by 60 publications

References 62 publications

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization

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