COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

• URL: http://arxiv.org/abs/2106.00436v1
• Date: Tue, 1 Jun 2021 12:33:08 GMT
• Title: COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network
• Authors: Tawsifur Rahman, Alex Akinbi, Muhammad E. H. Chowdhury, Tarik A. Rashid, Abdulkadir \c{S}eng\"ur, Amith Khandakar, Khandaker Reajul Islam, Aras M. Ismael
• Abstract summary: This study will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques.
• Score: 0.40631409309544836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consists of 1937 images from five distinct categories, such as Normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: two-class classification (Normal vs COVID-19); three-class classification (Normal, COVID-19, and Other CVDs), and finally, five-class classification (Normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities.

Related papers

• Auto-Lesion Segmentation with a Novel Intensity Dark Channel Prior for COVID-19 Detection [0.24578723416255752]
  This study develops a CT-based radiomics framework for differentiation of COVID-19 from other lung diseases. The model categorizes images into three classes: COVID-19, non-COVID-19, or normal. The best performing classification model, Residual Neural Network with 50 layers (Resnet-50), attained an average accuracy, precision, recall, and F1-score of 98.8%, 99%, 98%, and 98% respectively.
  arXiv  Detail & Related papers  (2023-09-22T06:09:48Z)
• Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks [7.10816643091298]
  Clinical reports indicated that electrocardiograms (ECG) may serve as a diagnostic biomarker for COVID-19. We propose a novel method to extract ECG signals from ECG paper records, which are fed into a one-dimensional convolution neural network (1D-CNN) to learn and diagnose the disease. Our proposed 1D-CNN model, which is trained on the digitized ECG signals, allows identifying individuals with COVID-19 and other subjects accurately.
  arXiv  Detail & Related papers  (2022-08-10T16:44:28Z)
• CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images [58.720142291102135]
  We propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN) For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones.
  arXiv  Detail & Related papers  (2021-11-16T15:03:42Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)
• COVID-VIT: Classification of COVID-19 from CT chest images based on vision transformer models [0.8594140167290099]
  This paper is responding to the MIA-COV19 challenge to classify COVID from non-COVID based on CT lung images. The overarching aim is to predict the diagnosis of the COVID-19 virus from chest radiographs. Two deep learning methods are studied, which are vision transformer (ViT) based on attention models and DenseNet that is built upon conventional convolutional neural network (CNN)
  arXiv  Detail & Related papers  (2021-07-04T16:55:51Z)
• COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning [70.92379567261304]
  We introduce COVID-Net CT-2, enhanced deep neural networks for COVID-19 detection from chest CT images. We leverage explainability to investigate the decision-making behaviour of COVID-Net CT-2. Results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment.
  arXiv  Detail & Related papers  (2021-01-19T03:04:09Z)
• RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray [0.0]
  COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) without the need for labels and training data.
  arXiv  Detail & Related papers  (2020-10-06T15:58:09Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images [75.74756992992147]
  We introduce COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images. We also introduce COVIDx-CT, a benchmark CT image dataset derived from CT imaging data collected by the China National Center for Bioinformation.
  arXiv  Detail & Related papers  (2020-09-08T15:49:55Z)
• Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks [4.294650528226683]
  COVID-19 is a respiratory syndrome that resembles pneumonia. Scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections. This article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches.
  arXiv  Detail & Related papers  (2020-04-23T10:24:34Z)
• Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images [46.844349956057776]
  coronavirus disease 2019 (COVID-19) has been spreading rapidly around the world and caused significant impact on the public health and economy. There is still lack of studies on effectively quantifying the lung infection caused by COVID-19. We propose a novel deep learning algorithm for automated segmentation of multiple COVID-19 infection regions.
  arXiv  Detail & Related papers  (2020-04-12T16:24:59Z)

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.