Detection of COVID-19 using Chest X-rays
7th IEEE International conference for Convergence in Technology, I2CT 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-1992593
ABSTRACT
We train a deep learning algorithm to flag potential covid-19 infected in chest x-rays. The deep learning algorithm used is a Convolutional Neural Network that is 121 layers deep. Due to the lack of a large open-source of covid-19 infected x-ray images, we combine data from five different sources. Combined, the dataset has 17,194 images that are used for training procedure. The model classifies a given chest X-ray image as either a "Normal", "Covid-19", or a 'Pneumonia"infection. The trained model has a 0.93 F1 Score and 93.496% accuracy. © 2022 IEEE.
chest x-ray; computer aided diagnosis; convolutional neural network; coronavirus; covid-19 detection; deep learning; Computer aided instruction; Convolution; Convolutional neural networks; Learning algorithms; Multilayer neural networks; Chest X-ray image; Chest x-rays; Coronaviruses; F1 scores; Open-source; Training procedures; X-ray image
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
7th IEEE International conference for Convergence in Technology, I2CT 2022
Year:
2022
Document Type:
Article
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