A GoogLeNet Performance Approach for COVID-19 Detection using Chest X-ray Images
15th International Conference on Knowledge and Smart Technology, KST 2023
; 2023.
Article
in English
| Scopus | ID: covidwho-2318489
ABSTRACT
Coronavirus disease (COVID-19) is a major pandemic disease that has already infected millions of people worldwide and affects many aspects, especially public health. There are many clinical techniques for the diagnosis of this disease, such as RT-PCR and CT-Scan. X-ray image is one of the important techniques for medical diagnosis and easily accessible in classifying suspected cases of COVID-19 infection. In this study, we classified COVID-19 images with four classes COVID-19, Normal, Lung opacity and Viral pneumonia by compared three models EfficientNetB0, MobileNet and GoogLeNet for the performance of classification using 1,000 chest X-ray images from Kaggle dataset within scenario of resource limitations. The experiment reveals that GoogLeNet shows superiority over other models that produced the highest accuracy results of 88% and F1 score of 0.88 with a total time of 1 hour and 15 minutes. Along with its confusion matrix that shows model can better classify images than other models. © 2023 IEEE.
Chest X-rays; Convolutional Neural Network; COVID-19; Deep Learning; Medical Image Classification; Classification (of information); Computer aided diagnosis; Computerized tomography; Convolutional neural networks; Image classification; Medical imaging; Chest X-ray; Chest X-ray image; Clinical techniques; Coronaviruses; CT-scan; Performance approach; X-ray image
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
15th International Conference on Knowledge and Smart Technology, KST 2023
Year:
2023
Document Type:
Article
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