Earticle

초록

영어

Since the onset of the Coronavirus outbreak in December 2019, the virus has infected over six hundred million individuals, resulting in more than six million confirmed deaths, as reported by the World Health Organization (WHO). COVID- 19 is attributed to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and is recognized as a respiratory ailment, characterized by symptoms including fever, myalgia, dry cough, headache, sore throat and chest pain. As of October 2022, substantial efforts have been directed toward understanding and combatting the disease, particularly in the domains of vaccination and diagnosis. This paper focuses on the diagnosis of COVID-19 using X-ray images and leverages deep learning technologies. Specifically, we concentrate on employing three convolutional neural network models: ResNet50, InceptionV3 and MobileNetV2. The primary objective is to evaluate their performance in diagnosing COVID-19 from Xray images. During our research, we subjected these models to testing with unseen data. The results revealed that ResNet50 achieved an accuracy of 82.5%, outperforming InceptionV3 with 62.5% and MobileNetV2 with 65% accuracy. The adoption of these models not only alleviates the decision-making burden on medical experts but also enhances the precision of disease classification. The significance of this study lies in its contribution to fine-tuning diagnostic algorithms, paving the way for further research and advancements in the field.

목차

Abstract
I. INTRODUCTION
II. RELATED WORKS
A. ResNet50
B. InceptionV3
C. MobileNetV2
III. METHODOLOGY
A. Data Collection
B. Data Preparation
C. Model Architecture and Configuration
D. Model Evaluation
E. Application
IV. CONCLUSION
REFERENCES

키워드

저자

• Warameth Nuipian [ Faculty of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok Thailand ]
• Phayung Meesad [ Faculty of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok Thailand ]
• Maleerat Maliyaem [ Faculty of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok Thailand ]
• Vatinee Nuipian [ Faculty of Technical Education King Mongkut's University of Technology North Bangkok Thailand ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004