@inproceedings{169ad2060b9848928811c17c41e9b0ba,
title = "Detection of Covid-19 Based on Lung Ultrasound Image Using Convolutional Neural Network Architectures",
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.",
keywords = "CNN, Covid-19 Detection, Ultrasound Medical Image, VGG16",
author = "Chastine Fatichah and Robby, {Muhammad Fadhlan Min} and Hidayati, {Shintami Chusnul} and Tanzilal Mustaqim",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021 ; Conference date: 16-12-2021",
year = "2021",
doi = "10.1109/ISRITI54043.2021.9702780",
language = "English",
series = "2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "155--160",
booktitle = "2021 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021",
address = "United States",
}