COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

Aboutalebi, Hossein; Pavlova, Maya; Shafiee, Mohammad Javad; Sabri, Ali; Alaref, Amer; Wong, Alexander

doi:10.3390/diagnostics12010025

Cited by 30 publications

(37 citation statements)

References 28 publications

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“…The lung images affected by COVID‐19 were gathered from various websites and research organizations. The affected as well as normal datasets have been included in the reference list ((https://www.kaggle.com/tawsifurrahman/covid19-radiography-database, n.d.; https://www.kaggle.com/c/dlai3/data, n.d.; Aboutalebi et al, 2021; Cohen et al, 2020; COVID, C. A. A, 2020; https://www.kaggle.com/c/dlai3/data, 2020)).…”

Section: Methodsmentioning

confidence: 99%

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In the proposed work three types of datasets images were resized. In the first dataset (Aboutalebi et al, 2021), 1200 COVID‐19 positive as well as 1200 negative images were checked and resized into 256*256 pixels. The rectangular lung images were resized into 256 pixels.…”

Section: Methodsmentioning

confidence: 99%

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A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

et al. 2021

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Background The COVID‐19 pandemic has precipitated global apprehensions about increased fatalities and raised concerns about gaps in healthcare infrastructure and accessibility the world over. Consequently, the importance of timely prediction and treatment of the disease to reduce transmission and mortality rates cannot be emphasized enough. Various symptoms of the disease have been identified as it progresses from the time it is contracted. COVID‐19 has been found to internally affect the lungs, and the four progressive stages of the infection can be categorized as mild, moderate, severe, and critical. Therefore, an accurate analysis of the current stage of the disease that can help predict its progression has become critical. X‐ray imaging has been found to be an effective screening procedure for predicting the various stages of this epidemic. Although many different approaches using machine learning, as well as deep learning were utilized to predict and classify diseases in general, till date, such an approach has not been used to predict the various stages of COVID‐19 by using X‐ray imaging to identify and classify those stages. Materials and method The proposed hybrid method used three public datasets for its implementation. In this work, extensive images were used for the purposes of testing and training. The dataset‐1 consists of 1200 COVID‐19 as well as 1200 Non‐COVID‐19 images, while dataset‐2 used 700 COVID‐19 as well as 700 Non‐COVID‐19 images, and finally, dataset‐III utilized 1900 COVID‐19 as well as 1900 Non‐COVID‐19 images for purposes of testing and training. The proposed work undertook the task of pre‐processing using textual and morphological features, while the segmentation and prediction of COVID‐19 as well as Non‐COVID‐19 images were undertaken using VGG‐16 with light GBM for better prediction and handing of huge datasets, and finally, the classification of the various stages of COVID‐19 images was performed using Deep Belief Network. Results The outcomes of the proposed work were subjected to several iterations which were then compared using different parameters such as accuracy, specificity, and sensitivity. In general, the prediction and grouping of the various stages of COVID‐19 by using affected images were found to be 99.2%, 99.4% and 99.5%, respectively. The bacterial pneumonia prediction rates were observed to be 98.5%, 99.4% and 98.3%, respectively. The average classification of the stages were found to be 98.1%, 98.6% and 98.3%, while the combined multi‐classification prediction rates were observed to be 98.6%, 99.1% and 98.7%, respectively.

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Section: Methodsmentioning

confidence: 99%

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Section: Methodsmentioning

confidence: 99%

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

et al. 2021

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“…A large majority of the proposed solutions tackled disease identification, based on deep learning algorithms [11,[14][15][16][17][18], providing some levels of interpretability [19][20][21]. Other studies approached different tasks like quantification of infection severity [22][23][24], segmentation of image [22,25], prediction of disease evolution [26] and image synthesis [27]. The aim of our study was to design a system for the prioritization, based on COVID-19 infection likelihood, of CXRs to support the diagnostic workflow [28,29].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary “Real Life” Results

et al. 2022

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The aim of our study is the development of an automatic tool for the prioritization of COVID-19 diagnostic workflow in the emergency department by analyzing chest X-rays (CXRs). The Convolutional Neural Network (CNN)-based method we propose has been tested retrospectively on a single-center set of 542 CXRs evaluated by experienced radiologists. The SARS-CoV-2 positive dataset (n = 234) consists of CXRs collected between March and April 2020, with the COVID-19 infection being confirmed by an RT-PCR test within 24 h. The SARS-CoV-2 negative dataset (n = 308) includes CXRs from 2019, therefore prior to the pandemic. For each image, the CNN computes COVID-19 risk indicators, identifying COVID-19 cases and prioritizing the urgent ones. After installing the software into the hospital RIS, a preliminary comparison between local daily COVID-19 cases and predicted risk indicators for 2918 CXRs in the same period was performed. Significant improvements were obtained for both prioritization and identification using the proposed method. Mean Average Precision (MAP) increased (p < 1.21 × 10−21 from 43.79% with random sorting to 71.75% with our method. CNN sensitivity was 78.23%, higher than radiologists’ 61.1%; specificity was 64.20%. In the real-life setting, this method had a correlation of 0.873. The proposed CNN-based system effectively prioritizes CXRs according to COVID-19 risk in an experimental setting; preliminary real-life results revealed high concordance with local pandemic incidence.

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“…(1) Most studies ( 19 , 21 , 23 , 24 ) focus on chest X-ray and CT imaging, and little work takes lung ultrasound images into consideration. (2) Existing methods ( 17 , 19 , 21 , 23 , 24 , 32 ) mostly leverage single-view images as input while it is more rational to exploit multi-view ones. (3) Existing methods ( 17 , 19 , 21 , 23 – 25 , 32 ) mostly utilize data of single modality (unitary CT, X-ray, ultrasound, or other modalities) while multi-modal data are conductive to offer more information.…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al ( 22 ) established a deep-learning-based diagnostic system to identify COVID-19 pneumonia. Aboutalebi et al ( 23 ) leveraged transfer learning to transfer representational knowledge for predicting the airspace severity of a SARS-CoV-2 positive patient based on CXR images. Amyar et al ( 24 ) proposed a new multitask deep learning model to jointly identify COVID-19 patients and segment COVID-19 lesions from chest CT images.…”

Section: Introductionmentioning

confidence: 99%

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform

Zhao

Gan

et al. 2022

Front. Public Health

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Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.

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COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

Cited by 30 publications

References 28 publications

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

A hybrid learning approach for the stage‐wise classification and prediction of COVID‐19 X‐ray images

Convolutional Neural Network-Based Automatic Analysis of Chest Radiographs for the Detection of COVID-19 Pneumonia: A Prioritizing Tool in the Emergency Department, Phase I Study and Preliminary “Real Life” Results

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform

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