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

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

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer 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

Originalspråkengelska
Titel på värdpublikation2021 Ethics and Explainability for Responsible Data Science (EE-RDS)
FörlagIEEE
Utgivningsdatum2021
ISBN (elektroniskt)9781665483582
DOI
StatusPublicerad - 2021
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangEthics and Explainability for Responsible Data Science Conference - Johannesburg, Sydafrika
Varaktighet: 27 okt. 202128 okt. 2021

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© 2021 IEEE.

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