COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach

• URL: http://arxiv.org/abs/2105.01710v1
• Date: Tue, 4 May 2021 19:01:40 GMT
• Title: COVID-19 Detection from Chest X-ray Images using Imprinted Weights Approach
• Authors: Jianxing Zhang, Pengcheng Xi, Ashkan Ebadi, Hilda Azimi, Stephane Tremblay, Alexander Wong
• Abstract summary: Chest radiography is an alternative screening method for the COVID-19. Computer-aided diagnosis (CAD) has proven to be a viable solution at low cost and with fast speed. To address this challenge, we propose the use of a low-shot learning approach named imprinted weights.
• Score: 67.05664774727208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The COVID-19 pandemic has had devastating effects on the well-being of the global population. The pandemic has been so prominent partly due to the high infection rate of the virus and its variants. In response, one of the most effective ways to stop infection is rapid diagnosis. The main-stream screening method, reverse transcription-polymerase chain reaction (RT-PCR), is time-consuming, laborious and in short supply. Chest radiography is an alternative screening method for the COVID-19 and computer-aided diagnosis (CAD) has proven to be a viable solution at low cost and with fast speed; however, one of the challenges in training the CAD models is the limited number of training data, especially at the onset of the pandemic. This becomes outstanding precisely when the quick and cheap type of diagnosis is critically needed for flattening the infection curve. To address this challenge, we propose the use of a low-shot learning approach named imprinted weights, taking advantage of the abundance of samples from known illnesses such as pneumonia to improve the detection performance on COVID-19.

Related papers

• A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images [0.0]
  COVID-19 was first identified in Wuhan and quickly spread worldwide. Most accessible method for COVID-19 identification is RT-PCR. Compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. DenseNet169 was employed to extract features from X-ray images.
  arXiv  Detail & Related papers  (2021-09-30T16:10:31Z)
• Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 [6.941255691176647]
  We propose a novel residual network to automatically identify COVID-19 from other common pneumonia and normal people using CT images. Our method can differentiate COVID-19 from the other two classes with 94.7% accuracy, 93.73% sensitivity, 98.28% specificity, 95.26% F1-score, and an area under the receiver operating characteristic curve (AUC) of 0.99.
  arXiv  Detail & Related papers  (2021-05-14T11:59:47Z)
• In-Line Image Transformations for Imbalanced, Multiclass Computer Vision Classification of Lung Chest X-Rays [91.3755431537592]
  This study aims to leverage a body of literature in order to apply image transformations that would serve to balance the lack of COVID-19 LCXR data. Deep learning techniques such as convolutional neural networks (CNNs) are able to select features that distinguish between healthy and disease states. This study utilizes a simple CNN architecture for high-performance multiclass LCXR classification at 94 percent accuracy.
  arXiv  Detail & Related papers  (2021-04-06T02:01:43Z)
• Deep Neural Networks for COVID-19 Detection and Diagnosis using Images and Acoustic-based Techniques: A Recent Review [0.36550217261503676]
  The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus.
  arXiv  Detail & Related papers  (2020-12-10T19:52:12Z)
• Pinball-OCSVM for early-stage COVID-19 diagnosis with limited posteroanterior chest X-ray images [3.4935179780034247]
  This research proposes a novel pinball loss function based one-class support vector machine (PB-OCSVM) that can work in presence of limited COVID-19 positive CXR samples. The performance of the proposed model is compared with conventional OCSVM and existing deep learning models, and the experimental results prove that the proposed model outperformed over state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-16T02:34:15Z)
• A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools [66.20818586629278]
  Coronavirus disease 2019 (COVID-19) has affected several million people. New screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19.
  arXiv  Detail & Related papers  (2020-09-04T00:50:27Z)
• Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images [41.73507451077361]
  We propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. We use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images.
  arXiv  Detail & Related papers  (2020-06-16T10:14:58Z)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)
• COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19 [92.4955073477381]
  The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Deep learning has been used recently as effective computer-aided means to improve diagnostic efficiency. We propose a new deep domain adaptation method for COVID-19 diagnosis, namely COVID-DA.
  arXiv  Detail & Related papers  (2020-04-30T03:13:40Z)
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection [86.81773672627406]
  Clusters of viral pneumonia during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images.
  arXiv  Detail & Related papers  (2020-03-27T11:32:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.