COVID-19 Detection from X-ray Images using Multi-Kernel-Size Spatial-Channel Attention Network

Fan, Yuqi; Yao, Ruixuan; Yuan, Xiaohui

doi:10.1016/j.patcog.2021.108055

Cited by 31 publications

(18 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It would be interesting to evaluate whether multi-modal analysis helps improving the accuracy of C19 detection: Image analysis is providing novel solutions using X-ray [50] , [51] , [52] , [53] , [54] , [55] and chest CT images [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] . Some of them [50] , [52] , [55] , [58] , [60] , [64] have discriminated C19 from another pulmonary disorder (pneumonia).…”

Section: Next Steps and Challengesmentioning

confidence: 99%

AI-Based human audio processing for COVID-19: A comprehensive overview

Deshpande

Batliner

Schuller

2022

Pattern Recognition

View full text Add to dashboard Cite

The Coronavirus (COVID-19) pandemic impelled several research efforts, from collecting COVID-19 patients’ data to screening them for virus detection. Some COVID-19 symptoms are related to the functioning of the respiratory system that influences speech production; this suggests research on identifying markers of COVID-19 in speech and other human generated audio signals. In this article, we give an overview of research on human audio signals using ’Artificial Intelligence’ techniques to screen, diagnose, monitor, and spread the awareness about COVID-19. This overview will be useful for developing automated systems that can help in the context of COVID-19, using non-obtrusive and easy to use bio-signals conveyed in human non-speech and speech audio productions.

show abstract

Section: Next Steps and Challengesmentioning

confidence: 99%

AI-Based human audio processing for COVID-19: A comprehensive overview

Deshpande

Batliner

Schuller

2022

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…The bulk of literature addressing the task of classifying CXRs for COVID-19 is largely based on deep supervised learning e.g. [9] , [13] , [14] , [15] , [16] , [18] , [19] , [31] , [32] , [33] , and several techniques arise every single day. Most of approaches apply pre-trained off-the-shelf networks; in which diverse generic architectures, including ResNet [34] , DenseNet [35] and VGG [36] .…”

Section: Related Workmentioning

confidence: 99%

“…For the task of classifying COVID-19 using CXRs data, there has been a fast development of deep learning techniques e.g. [13] , [14] , [15] , [16] , [17] , [18] , [19] , in which supervised learning is the go-to paradigm. However, the performance of these techniques strongly rely on a large and representative corpus of labelled data.…”

Section: Introductionmentioning

confidence: 99%

GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays

Avilés-Rivero

Sellars

Schönlieb

et al. 2022

Pattern Recognition

View full text Add to dashboard Cite

Can one learn to diagnose COVID-19 under extreme minimal supervision? Since the outbreak of the novel COVID-19 there has been a rush for developing automatic techniques for expert-level disease identification on Chest X-ray data. In particular, the use of deep supervised learning has become the go-to paradigm. However, the performance of such models is heavily dependent on the availability of a large and representative labelled dataset. The creation of which is a heavily expensive and time consuming task, and especially imposes a great challenge for a novel disease. Semi-supervised learning has shown the ability to match the incredible performance of supervised models whilst requiring a small fraction of the labelled examples. This makes the semi supervised paradigm an attractive option for identifying COVID-19. In this work, we introduce a graph based deep semi-supervised framework for classifying COVID-19 from chest X-rays. Our framework introduces an optimisation model for graph diffusion that reinforces the natural relation among the tiny labelled set and the vast unlabelled data. We then connect the diffusion prediction output as pseudo-labels that are used in an iterative scheme in a deep net. We demonstrate, through our experiments, that our model is able to outperform the current leading supervised model with a tiny fraction of the labelled examples. Finally, we provide attention maps to accommodate the radiologist’s mental model, better fitting their perceptual and cognitive abilities. These visualisation aims to assist the radiologist in judging whether the diagnostic is correct or not, and in consequence to accelerate the decision.

show abstract

“…The authors in [42], suggest a model for categorizing X---ray images based on a novel multi---kernel---size spatial---channel attention model. With a binary---class database (Corona--virus, and normal), the proposed model accomplishes an accuracy of 98.2%.…”

Section: Related Workmentioning

confidence: 99%

Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique

et al. 2021

View full text Add to dashboard Cite

Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of COVID-19 is extremely important for the medical community to limit its spread. For a large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish COVID-19 precisely in chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., COVID-19 case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to confirm the reliability of the proposed method for identifying the patients with COVID-19 disease from X-ray images. The proposed system was trialed on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and COVID-19 cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%.

show abstract

COVID-19 Detection from X-ray Images using Multi-Kernel-Size Spatial-Channel Attention Network

Cited by 31 publications

References 16 publications

AI-Based human audio processing for COVID-19: A comprehensive overview

AI-Based human audio processing for COVID-19: A comprehensive overview

GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays

Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique

Contact Info

Product

Resources

About