Deep learning with concatenate model to detect COVID-19 lung disease with CT scan images

Alrafiful Rahman, Alhadi Bustamam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The current dangerous viral disease is COVID-19. This viral disease can spread quickly and not only to humans, but animals can also contract the COVID-19 virus disease. Until now, the spread of the COVID-19 virus has not been able to stop. The COVID-19 virus disease is mostly caused by lung infections. To diagnose COVID-19 virus disease with more effective imaging using CT scan images. DenseNet121, MobileNet, Xception, InceptionV3, ResNet50V2, and VGG19 models to check their accuracy in image recognition. To analyze the model performance, 1361 samples from CT scans were collected from the Kaggle warehouse. The DenseNet121-MobileNet model has a sensitivity of 99.76%, a specificity of 99.63%, and an accuracy of 99.76% with a computation time faster. This work focuses only on the methods used to detect patients with COVID-19 in the lungs but does not mention any medical accuracy.

Original languageEnglish
Title of host publicationInternational Conference on Science and Applied Science, ICSAS 2021
EditorsBudi Purnama, Dewanta Arya Nugraha, A. Suparmi
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735441859
DOIs
Publication statusPublished - 24 Mar 2022
Event2021 International Conference on Science and Applied Science, ICSAS 2021 - Surakarta, Virtual, Indonesia
Duration: 6 Apr 20216 Apr 2021

Publication series

NameAIP Conference Proceedings
Volume2391
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2021 International Conference on Science and Applied Science, ICSAS 2021
Country/TerritoryIndonesia
CitySurakarta, Virtual
Period6/04/216/04/21

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