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

arXiv: https://arxiv.org/abs/2106.00436 · HTML: https://mlsanity.com/paper/2106.00436/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 63 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [SmoothGrad: removing noise by adding noise](https://mlsanity.com/paper/1706.03825/) (2017)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?](https://mlsanity.com/paper/1706.00712/) (2017)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization](https://mlsanity.com/paper/2007.14895/) (2020)
- [COVID-19 Infection Map Generation and Detection from Chest X-Ray Images](https://mlsanity.com/paper/2009.12698/) (2020)
- [Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray](https://mlsanity.com/paper/2004.06578/) (2020)
- [Detection and severity classification of COVID-19 in CT images using deep learning](https://mlsanity.com/paper/2102.07726/) (2021)
- [Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network](https://mlsanity.com/paper/2101.01666/) (2020)
- [EDITH :ECG biometrics aided by Deep learning for reliable Individual auTHentication](https://mlsanity.com/paper/2102.08026/) (2021)
- [An early warning tool for predicting mortality risk of COVID-19 patients using machine learning](https://mlsanity.com/paper/2007.15559/) (2020)
- [An Intelligent and Low-cost Eye-tracking System for Motorized Wheelchair Control](https://mlsanity.com/paper/2005.02118/) (2020)

## Built on by (most cited first, 6 of 6)

- [LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification](https://mlsanity.com/paper/2207.12381/) (2022)
- [Survival and grade of the glioma prediction using transfer learning](https://mlsanity.com/paper/2402.03384/) (2024)
- [Telehealthcare and Telepathology in Pandemic: A Noninvasive, Low-Cost Micro-Invasive and Multimodal Real-Time Online Application for Early Diagnosis of COVID-19 Infection](https://mlsanity.com/paper/2109.07846/) (2021)
- [COVID-19 Electrocardiograms Classification using CNN Models](https://mlsanity.com/paper/2112.08931/) (2021)
- [Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration](https://mlsanity.com/paper/2208.07088/) (2022)
- [Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale](https://mlsanity.com/paper/2211.10431/) (2022)

## Abstract (arXiv)

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.