Abstract

The spread of Covid-19 is so fast that it has become a global pandemic. A fast, cheap, and guaranteed Covid-19 detection system is needed. Medical images such as CT scans and X-rays with biological sciences and deep learning techniques can be critical diagnostic tools. This study uses ultrasound images as an alternative to medical images that can diagnose Covid-19 using a deep learning method based on the Convolutional Neural Network (CNN) architectures. The dataset used is obtained from the Covid-19 Lung Ultrasound. This study shows the highest accuracy of detection covid-19 based on a lung ultrasound image using the VGG16 architecture compared to ResNet50 and InceptionV3architectures. VGG16 architecture with an Adam optimization and a learning rate of 0.0001 yielded 86% accuracy. ResNet50 and InceptionV3architectures produce 79% and 64% of accuracy.

Original languageEnglish
Title of host publication2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages155-160
Number of pages6
ISBN (Electronic)9781665401517
DOIs
Publication statusPublished - 2021
Event4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021 - Virtual, Yogyakarta, Indonesia
Duration: 16 Dec 2021 → …

Publication series

Name2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021

Conference

Conference4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
Country/TerritoryIndonesia
CityVirtual, Yogyakarta
Period16/12/21 → …

Keywords

  • CNN
  • Covid-19 Detection
  • Ultrasound Medical Image
  • VGG16

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