Detection of Covid-19 Based on Lung Ultrasound Image Using Convolutional Neural Network Architectures
4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
; : 155-160, 2021.
Article
in English
| Scopus | ID: covidwho-1769642
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. © 2021 IEEE.
CNN; Covid-19 Detection; Ultrasound Medical Image; VGG16; Biological organs; Computerized tomography; Convolution; Deep learning; Diagnosis; Medical imaging; Network architecture; Ultrasonic applications; Biological science; Convolutional neural network; CT-scan; Detection system; Lung ultrasound; Neural network architecture; Ultrasound images; Ultrasound medical images; Convolutional neural networks
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021
Year:
2021
Document Type:
Article
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