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A Comparative Study of Deep Learning Models for Pneumonia Detection: CNN, VUNO, LUIT Models

폐렴 및 정상군 판별을 위한 딥러닝 모델 성능 비교연구: CNN, VUNO, LUNIT 모델 중심으로

  • Ji-Hyeon Lee (Marykynoll Medical Center) ;
  • Soo-Young Ye (Department of Radiological Science, Catholic University of Pusan)
  • 이지현 (부산가톨릭대학교 방사선학과) ;
  • 예수영 (부산가톨릭대학교 방사선학과)
  • Received : 2024.07.22
  • Accepted : 2024.08.12
  • Published : 2024.09.30

Abstract

The purpose of this study is to develop a CNN based deep learning model that can effectively detect pneumonia by analyzing chest X-ray images of adults over the age of 20 and compare it with VUNO, LUNIT a commercialized AI model. The data of chest X-ray image was evaluate based on accuracy, precision, recall, F1 score, and AUC score. The CNN model recored an accuracy of 82%, precision 76%, recall 99%, F1 score 86%, and AUC score 0.7937. The VUNO model recordded an accuracy of 84%, precision 81%, recall 94%, F1 score 87%, and AUC score 0.8233. The LUNIT model recorded an accuracy of 77%, precision 72%, recall 96%, F1 score 83%, and AUC score 0.7436. As a result of the Confusion Matrix analysis, the CNN model showe FN (3), showing the highest recall rate (99%) in the diagnosis of pneumonia. The VUNO model showed excellent overall perfomance with high accuracy (84%) and AUC score (0.8233), and the LUNIT model showed high recall rate (96%) but the accuracy and precision showed relatively low results. This study will be able to provide basic data useful for the development of a pneumonia diagnosis system by comprehensively considers the perfomance of the medel is necessary to effectively discriminate between penumonia and normal groups.

Keywords

References

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