COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 From Chest CT Images Through Bigger, More Diverse Learning

Abstract

The COVID-19 pandemic continues to rage on, with multiple waves causing substantial harm to health and economies around the world. Motivated by the use of computed tomography (CT) imaging at clinical institutes around the world as an effective complementary screening method to RT-PCR testing, we introduced COVID-Net CT, a deep neural network tailored for detection of COVID-19 cases from chest CT images, along with a large curated benchmark dataset comprising 1,489 patient cases as part of the open-source COVID-Net initiative. However, one potential limiting factor is restricted data quantity and diversity given the single nation patient cohort used in the study. To address this limitation, in this study we introduce enhanced deep neural networks for COVID-19 detection from chest CT images which are trained using a large, diverse, multinational patient cohort. We accomplish this through the introduction of two new CT benchmark datasets, the largest of which comprises a multinational cohort of 4,501 patients from at least 16 countries. To the best of our knowledge, this represents the largest, most diverse multinational cohort for COVID-19 CT images in open-access form. Additionally, we introduce a novel lightweight neural network architecture called COVID-Net CT S, which is significantly smaller and faster than the previously introduced COVID-Net CT architecture. We leverage explainability to investigate the decision-making behavior of the trained models and ensure that decisions are based on relevant indicators, with the results for select cases reviewed and reported on by two board-certified radiologists with over 10 and 30 years of experience, respectively. The best-performing deep neural network in this study achieved accuracy, COVID-19 sensitivity, positive predictive value, specificity, and negative predictive value of 99.0%/99.1%/98.0%/99.4%/99.7%, respectively. Moreover, explainability-driven performance validation shows consistency with radiologist interpretation by leveraging correct, clinically relevant critical factors. The results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment. While not a production-ready solution, we hope the open-source, open-access release of COVID-Net CT-2 and the associated benchmark datasets will continue to enable researchers, clinicians, and citizen data scientists alike to build upon them.

COVID-Net US: A Tailored, Highly Efficient, Self-Attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-care Ultrasound Imaging

“…There are a multitude of initiatives aiming to apply machine learning to classification of medical images for screening of COVID-19 infections. One such initiative is the COVID-Net initiative, which has shown success in curating opensource publicly available CXR and CT image datasets to act as training data for deep neural network models, and in using machine-driven design exploration to optimize both micro and macroarchitecture to build an architecture tailored to the problem at hand, improving both efficacy and efficiency [16,8,21,7].…”

“…Finally, to demonstrate the efficacy of the proposed COVID-Net US for edge devices, we evaluate its practical performance by deploying it on a Raspberry Pi with a 1.5 GHz ARM Cortex 72 CPU with 4GB memory. To advocate affordable healthcare and artificial intelligence for resource-constrained environments, we have made COVID-Net US open source and publicly available as part of the COVID-Net open source initiative 7 [16,8,21,7] for accelerating the advancement and adoption of deep learning for tackling this pandemic.…”

A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images

“…The COVID-MaskNet [20] uses a segmentation network to localize the disease lesion, then use a FasterCNNbased approach to do the classification on the detected lesion regions. The COVID-Net Initiative [6], [5] have done extensive studies of COVID classification on both CT scan images and X-ray images. They also collect and publish the largest CT image dataset -so called COVIDx CT-2 dataset.…”

Insights into Data through Model Behaviour: An Explainability-driven Strategy for Data Auditing for Responsible Computer Vision Applications

“…4 for example CT images from the OSIC Pulmonary Fibrosis Progression dataset and corresponding identified critical factors). In the case of CNCB COVID-19 CT dataset, addressing the discovered data quality issues led to the creation of deep CNN classification models [5,4] with state-of-the-art performance (exceeding 98% accuracy) that learned to leverage relevant visual anomalies in the lungs such as ground-glass opacities and bilateral bilateral patchy opacities.…”