A Review on the State of the Art in Non Contact Sensing for COVID-19

Taylor, William R.; Abbasi, Qammer H.; Dashtipour, Kia; Ansari, Shuja; Shah, Aziz; Khan, Arslan; Imran, Muhammad Ali

doi:10.48550/arxiv.2007.16063

Cited by 2 publications

(3 citation statements)

References 53 publications

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“…The aims of this study are (1) to classify a CXR image into one of the four classes: Health, Bacterial pneumonia, viral pneumonia of non infectious origin other than COVID-19 (ViralN) and COVID-19, and (2) to highlight the virus infected area on the image simultaneously. Therefore, we proposed a novel deep learning framework for classification and pixelbased visual explanation in an end-to-end manner.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images

Zhang¹,

Han²,

Sobeih³

et al. 2021

Preprint

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Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first line imaging test for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Inspired by the success of deep learning (DL) in computer vision, many DL-models have been proposed to detect COVID-19 pneumonia using CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing commonly used visual explanation methods are either too noisy or imprecise, with low resolution, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable deep learning framework (CXRNet) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation from CXR images. The proposed framework is based on a new Encoder-Decoder-Encoder multitask architecture, allowing for both disease classification and visual explanation. The method has been evaluated on real world CXR datasets from both public and private data sources, including: healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases The experimental results demonstrate that the proposed method can achieve a satisfactory level of accuracy and provide fineresolution classification activation maps for visual explanation in lung disease detection. The Average Accuracy, the Precision, Recall and F1-score of COVID-19 pneumonia reached 0.879, 0.985, 0.992 and 0.989, respectively. We have also found that using lung segmented (CXR) images can help improve the performance of the model. The proposed method can provide more detailed high resolution visual explanation for the classification decision, compared to current state-of-the-art visual explanation methods and has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.

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Section: The Proposed Methodsmentioning

confidence: 99%

“…S Ince December 2019, the world is experiencing a global pandemic from the emergence and spread of the potentially fatal COVID-19 (COronaVIrus Disease 2019) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. To optimize the management of this disease, accurate and rapid diagnosis is essential.…”

Section: Introductionmentioning

confidence: 99%

CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images

Zhang¹,

Han²,

Sobeih³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…S Ince December 2019, the world has been experiencing a global pandemic from the emergence and spread of the potentially fatal COVID-19 (COronaVIrus Disease 2019) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. To optimise the management of this disease, accurate and rapid diagnosis is essential.…”

Section: Introductionmentioning

confidence: 99%

CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia From Chest X-Ray Images

Zhang

Han

Sobeih

et al. 2023

IEEE J. Biomed. Health Inform.

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Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learning (DL) models have been proposed to detect COVID-19 pneumonia from CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing explanation methods produce either too noisy or imprecise results, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable CXR deep neural Network (CXR-Net) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation using CXR images. An Encoder-Decoder-Encoder architecture is proposed, in which an extra encoder is added after the encoder-decoder structure to ensure the model can be trained on category samples. The method has been evaluated on real world CXR datasets from both public and private sources, including healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases. The results demonstrate that the proposed method can achieve a satisfactory accuracy and provide fine-resolution activation maps for visual explanation in the lung disease detection. The Average Accuracy, Sensitivity, Specificity, PPV and F1-score of models in the COVID-19 pneumonia detection reach 0.992, 0.998, 0.985 and 0.989, respectively. Compared to current state-of-the-art visual explanation methods, the proposed method can provide more detailed, high-resolution, visual explanation for the classification results. It can be deployed in various computing environments, including cloud, CPU and GPU environments. It has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.

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A Review on the State of the Art in Non Contact Sensing for COVID-19

Cited by 2 publications

References 53 publications

CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images

CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images

CXR-Net: A Multitask Deep Learning Network for Explainable and Accurate Diagnosis of COVID-19 Pneumonia From Chest X-Ray Images

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