scholarly journals PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images

2020 ◽  
Author(s):  
Nihad K Chowdhury ◽  
Muhtadir Rahman ◽  
Muhammad Ashad Kabir
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Performance Metrics ◽  
Detection System ◽  
Feature Maps ◽  
Screening Techniques ◽  
X Ray ◽  
Support Resistance ◽  
Chest X Ray

The COVID-19 pandemic continues to severely undermine the prosperity of the global health system. To combat this pandemic, effective screening techniques for infected patients are indispensable. There is no doubt that the use of chest X-ray images for radiological assessment is one of the essential screening techniques. Some of the early studies revealed that the patient’s chest X-ray images showed abnormalities, which is natural for patients infected with COVID-19. In this paper,we proposed a parallel-dilated convolutional neural network (CNN) based COVID-19 detection system from chest x-ray images, named as Parallel-Dilated COVIDNet (PDCOVIDNet). First, the publicly available chest X-ray collection fully preloaded and enhanced, and then classified by the proposed method. Differing convolution dilation rate in a parallel form demonstrates the proof-of-principle for using PDCOVIDNet to extract radiological features for COVID-19 detection. Accordingly, we have assisted our method with two visualization methods, which are specifically designed to increase understanding of the key components associated with COVID-19 infection. Both visualization methods compute gradients for a given image category related to feature maps of the last convolutional layer to create a class-discriminative region. In our experiment, we used a total of 2,905 chest X-ray images, comprising three cases (such as COVID-19, normal, and viral pneumonia), and empirical evaluations revealed that the proposed method extracted more significantfeatures expeditiously related to suspected disease. The experimental results demonstrate that our proposed method significantly improves performance metrics: the accuracy, precision, recall and F1 scores reach 96.58%, 96.58%, 96.59% and 96.58%, respectively, which is comparable or enhanced compared with the state-of-the-art methods. We believe that our contribution can support resistance to COVID-19, and will adopt for COVID-19 screening in AI-based systems.

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

Diagnostics ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
Hossein Aboutalebi ◽  
Maya Pavlova ◽  
Mohammad Javad Shafiee ◽  
Ali Sabri ◽  
Amer Alaref ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Clinical Decision ◽  
Severity Assessment ◽  
Clinical Workflow ◽  
X Ray ◽  
Number Of Patients ◽  
The World ◽  
Chest X Ray

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

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

2021 ◽  
Author(s):  
Hossein Aboutalebi ◽  
Maya Pavlova ◽  
Mohammad Javad Shafiee ◽  
Ali Sabri ◽  
Amer Alaref ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Clinical Decision ◽  
Severity Assessment ◽  
Clinical Workflow ◽  
X Ray ◽  
Number Of Patients ◽  
The World ◽  
Chest X Ray

Abstract The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest x-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient's chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 patient cases into a custom network architecture for severity assessment. Experimental results with a multi-national patient cohort curated by the Radiological Society of North America (RSNA) RICORD initiative showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic.

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