COVID-19 Detection via Image Classification using Deep Learning on Chest X-Ray

Mohammad Ayyaz Azeem; Muhammad Irfan Khan; Suleiman A. Khan

doi:10.1109/EE-RDS53766.2021.9708588

COVID-19 Detection via Image Classification using Deep Learning on Chest X-Ray

Mohammad Ayyaz Azeem, Muhammad Irfan Khan, Suleiman A. Khan

Institutet för molekylärmedicin i Finland

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

Sammanfattning

The focus of this study is to evaluate and examine a set of deep learning transfer learning techniques applied to chest radiograph images for the classification of COVID-19, normal (healthy), and pneumonia. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. Our results indicate that the VGG16 method outperforms comparative classification models in terms of accuracy, sensitivity, and specificity. The VGG16 model detects and classifies COVID-19, normal (healthy), and pneumonia with 94% test accuracy, 94% sensitivity, and 94.20% specificity. Code is publically available at: https://github.com/ayyaz-azeem/Covid19challenge.git

Originalspråk	engelska
Titel på värdpublikation	2021 Ethics and Explainability for Responsible Data Science (EE-RDS)
Förlag	IEEE
Utgivningsdatum	2021
ISBN (elektroniskt)	978-1-6654-8358-2
DOI	https://doi.org/10.1109/EE-RDS53766.2021.9708588
Status	Publicerad - 2021
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	Ethics and Explainability for Responsible Data Science Conference - Johannesburg, Sydafrika Varaktighet: 27 okt. 2021 → 28 okt. 2021

Bibliografisk information

Publisher Copyright:
© 2021 IEEE.

Vetenskapsgrenar

113 Data- och informationsvetenskap

Åtkomst av dokument

10.1109/EE-RDS53766.2021.9708588

Citera det här

@inproceedings{d7fd60c86f864c05bddd0ac87886ab0d,

title = "COVID-19 Detection via Image Classification using Deep Learning on Chest X-Ray",

abstract = "The focus of this study is to evaluate and examine a set of deep learning transfer learning techniques applied to chest radiograph images for the classification of COVID-19, normal (healthy), and pneumonia. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. Our results indicate that the VGG16 method outperforms comparative classification models in terms of accuracy, sensitivity, and specificity. The VGG16 model detects and classifies COVID-19, normal (healthy), and pneumonia with 94% test accuracy, 94% sensitivity, and 94.20% specificity. Code is publically available at: https://github.com/ayyaz-azeem/Covid19challenge.git",

keywords = "COVID-19 classification, COVID-19 detection, CXR images, Deep Learning, Machine Learning, Transfer Learning, VGG16, X-Ray, 113 Computer and information sciences",

author = "Azeem, {Mohammad Ayyaz} and Khan, {Muhammad Irfan} and Khan, {Suleiman A.}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; Ethics and Explainability for Responsible Data Science Conference, EE-RDS ; Conference date: 27-10-2021 Through 28-10-2021",

year = "2021",

doi = "10.1109/EE-RDS53766.2021.9708588",

language = "English",

booktitle = "2021 Ethics and Explainability for Responsible Data Science (EE-RDS)",

publisher = "IEEE",

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}

TY - GEN

T1 - COVID-19 Detection via Image Classification using Deep Learning on Chest X-Ray

AU - Azeem, Mohammad Ayyaz

AU - Khan, Muhammad Irfan

AU - Khan, Suleiman A.

PY - 2021

Y1 - 2021

N2 - The focus of this study is to evaluate and examine a set of deep learning transfer learning techniques applied to chest radiograph images for the classification of COVID-19, normal (healthy), and pneumonia. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. Our results indicate that the VGG16 method outperforms comparative classification models in terms of accuracy, sensitivity, and specificity. The VGG16 model detects and classifies COVID-19, normal (healthy), and pneumonia with 94% test accuracy, 94% sensitivity, and 94.20% specificity. Code is publically available at: https://github.com/ayyaz-azeem/Covid19challenge.git

AB - The focus of this study is to evaluate and examine a set of deep learning transfer learning techniques applied to chest radiograph images for the classification of COVID-19, normal (healthy), and pneumonia. In this work, we have used four transfer learning models, VGG16, InceptionV3, ResNet50, and DenseNet121 for the classification tasks. Our results indicate that the VGG16 method outperforms comparative classification models in terms of accuracy, sensitivity, and specificity. The VGG16 model detects and classifies COVID-19, normal (healthy), and pneumonia with 94% test accuracy, 94% sensitivity, and 94.20% specificity. Code is publically available at: https://github.com/ayyaz-azeem/Covid19challenge.git

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KW - COVID-19 detection

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KW - Machine Learning

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