[논문]InceptionV3 기반의 심장비대증 분류 정확도 향상 연구

정우연; 김정훈

doi:10.9718/jber.2022.43.1.45

InceptionV3 기반의 심장비대증 분류 정확도 향상 연구
A Study on the Improvement of Accuracy of Cardiomegaly Classification Based on InceptionV3 원문보기

Journal of biomedical engineering research : the official journal of the Korean Society of Medical & Biological Engineering, v.43 no.1, 2022년, pp.45 - 51

정우연 (경북대학교대학원 의용생체공학과) , 김정훈 (경북대학교병원 생명의학연구원)

Abstract ▼ AI-Helper

The purpose of this study is to improve the classification accuracy compared to the existing InceptionV3 model by proposing a new model modified with the fully connected hierarchical structure of InceptionV3, which showed excellent performance in medical image classification. The data used for model training were trained after data augmentation on a total of 1026 chest X-ray images of patients diagnosed with normal heart and Cardiomegaly at Kyungpook National University Hospital. As a result of the experiment, the learning classification accuracy and loss of the InceptionV3 model were 99.57% and 1.42, and the accuracy and loss of the proposed model were 99.81% and 0.92. As a result of the classification performance evaluation for precision, recall, and F1 score of Inception V3, the precision of the normal heart was 78%, the recall rate was 100%, and the F1 score was 88. The classification accuracy for Cardiomegaly was 100%, the recall rate was 78%, and the F1 score was 88. On the other hand, in the case of the proposed model, the accuracy for a normal heart was 100%, the recall rate was 92%, and the F1 score was 96. The classification accuracy for Cardiomegaly was 95%, the recall rate was 100%, and the F1 score was 97. If the chest X-ray image for normal heart and Cardiomegaly can be classified using the model proposed based on the study results, better classification will be possible and the reliability of classification performance will gradually increase.

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표/그림 (8)

표 표 1. 연구에 사용된 개발환경 Table 1. Development environment used for research
표 표 2. 딥러닝 모델 구현환경 Table 2. Deep learning model implementation environment
그림 그림 1. 기존 InceptionV3의 구조와 제안된 InceptionV3의 구조 Fig. 1. The structure of the original InceptionV3 and the structure of the proposed InceptionV3
표 표 3. 사용된 학습, 검증, 테스트 데이터의 구성 Table 3. Compose of images in train, validation, test data
그림 그림 2. AVIEW 프로그램을 이용한 흉부 X-ray 영상에서의 심장 Annotation (A) Annotation 전 데이터 (B) Annotation 후 데이터 Fig. 2. Heart Annotation on Chest X-ray Image Using AVIEW Program
그림 그림 3. InceptionV3와 제안된 InceptionV3의 분류 정확도 및 손실 (A) InceptionV3 (B) 제안된 InceptionV3 Fig. 3. Classification accuracy and loss of InceptionV3 and Proposed InceptionV3 (A) InceptionV3 (B) Proposed InceptionV3
표 표 4. InceptionV3 모델의 정밀도, 재현율, F1 Score 평가 결과 Table 4. Precision, recall, and F1 Score evaluation results of InceptionV3 model
표 표 5. 제안하는 InceptionV3 모델의 정밀도, 재현율, F1 Score 평가 결과 Table 5. Precision, recall, and F1 Score evaluation results of the proposed InceptionV3 model

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