[논문]저선량 흉부 CT를 이용한 VGGNet 폐기종 검출 유용성 평가

김두빈; 박영준; 홍주완

doi:10.7742/jksr.2022.16.4.411

저선량 흉부 CT를 이용한 VGGNet 폐기종 검출 유용성 평가
Effectiveness of the Detection of Pulmonary Emphysema using VGGNet with Low-dose Chest Computed Tomography Images 원문보기

한국방사선학회 논문지 = Journal of the Korean Society of Radiology, v.16 no.4, 2022년, pp.411 - 417

김두빈 (한국의학연구소) , 박영준 (제주한라대학교 보건학부 방사선과) , 홍주완 (을지대학교 보건과학대학 방사선학과)

초록
AI-Helper

본 연구에서는 저선량 흉부 CT 영상을 이용하여 VGGNet을 학습시키고 폐기종 검출 모델을 구현하고 성능을 확인하고자 한다. 연구에 사용된 저선량 흉부 CT 영상은 정상 진단 8000장, 폐기종 진단 3189장이며, 모델 학습을 위해 정상 데이터와 폐기종 데이터를 train, validation, test dataset으로 각각 60%, 24%, 16%로 무작위 추출하여 구분하였다. 학습을 위한 인공신경망은 VGGNet 중 VGG16과 VGG19를 사용하였으며, 학습이 완료된 모델 평가를 위해 정확도, 손실율, 오차 행렬, 정밀도, 재현율, 특이도, F1-score의 평가지표를 사용하였다. 폐기종 검출 정확도와 손실율은 VGG16과 VGG19 각각 92.35%, 95.88%, 0.21%, 0.09%, 정밀도는 91.60%, 96.55%, 재현율은 98.36%, 97.39%, 특이도는 77.08%, 92.72%, F1-score는 94.86%, 96.97%였다. 위의 평가지표를 통해 VGG19 모델의 폐기종 검출 성능이 VGG16 모델에 비해 우수하다고 판단된다. 본 연구를 통해 VGGNet과 인공신경망을 이용한 폐기종 검출 모델 연구에 기초자료로 사용할 수 있을 것으로 사료된다.

Abstract ▼ AI-Helper

This study aimed to learn and evaluate the effectiveness of VGGNet in the detection of pulmonary emphysema using low-dose chest computed tomography images. In total, 8000 images with normal findings and 3189 images showing pulmonary emphysema were used. Furthermore, 60%, 24%, and 16% of the normal and emphysema data were randomly assigned to training, validation, and test datasets, respectively, in model learning. VGG16 and VGG19 were used for learning, and the accuracy, loss, confusion matrix, precision, recall, specificity, and F1-score were evaluated. The accuracy and loss for pulmonary emphysema detection of the low-dose chest CT test dataset were 92.35% and 0.21% for VGG16 and 95.88% and 0.09% for VGG19, respectively. The precision, recall, and specificity were 91.60%, 98.36%, and 77.08% for VGG16 and 96.55%, 97.39%, and 92.72% for VGG19, respectively. The F1-scores were 94.86% and 96.97% for VGG16 and VGG19, respectively. Through the above evaluation index, VGG19 is judged to be more useful in detecting pulmonary emphysema. The findings of this study would be useful as basic data for the research on pulmonary emphysema detection models using VGGNet and artificial neural networks.

주제어

표/그림 (4)

그림 Fig. 1. Low-dose chest CT images, (A) was normal chest CT image and (B) was emphysema chest CT image.
그림 Fig. 2. Accuracy and loss graphs of VGGNet according to epochs.
그림 Fig. 3. Confusion matrix of VGGNet.
표 Table 1. Results of each evaluation index Based on Learning (unit : %)

AI 본문요약
AI-Helper

문제 정의

Additionally, various other models using radiographic images are available; however, there is a lack of disease detection models using low-dose chest CT images. Therefore, this study aimed to develop and evaluate a CNN-based VGGNet model through transfer learning for detecting pulmonary emphysema using low-dose chest CT images.

제안 방법

In total, 11,189 images were used for learning, including 8000 images with normal findings and 3189 images of pulmonary emphysema. For efficient learning, 60%, 24%, and 16% of the normal and emphysema data were randomly assigned for training, validation, and test datasets, respectively. The size of the input data used for learning was converted and fixed to 224 × 224.
For the evaluation of the model in which the learning was completed, the accuracy and loss, confusion matrix, precision, recall, specificity, and F1-score for the training and validation data were calculated and evaluated. The calculation formula of each indicator is as shown in (4) ~ (7) below.

대상 데이터

Linux, Intel, GeForce, and RAM were used as the learning systems. In total, 11,189 images were used for learning, including 8000 images with normal findings and 3189 images of pulmonary emphysema. For efficient learning, 60%, 24%, and 16% of the normal and emphysema data were randomly assigned for training, validation, and test datasets, respectively.
The data used in this study were collected from patients who underwent low-dose chest CT from January 2020 to January 2021 (IRB approval number EU22-16). In total, 8000 images with normal findings and 3189 images showing pulmonary emphysema were collected.
The data used in this study were collected from patients who underwent low-dose chest CT from January 2020 to January 2021 (IRB approval number EU22-16). In total, 8000 images with normal findings and 3189 images showing pulmonary emphysema were collected.

이론/모형

VGG16 and VGG19 algorithms were used. The general working principle of VGGNet is to apply a 3 × 3 convolutional filter to the convolution layer for extraction of the key features and prevent overfitting through max pooling with 2 × 2 size.

성능/효과

In this study, VGGNet was useful in detecting pulmonary emphysema using low-dose chest CT images. The findings of this study may be used as basic data for studies on customized models for detecting pulmonary emphysema.
The model using LSTM showed superior results when compared to the model using MIL[15]. The LSTM model had 77% recall, 74% specificity, and 75% F1-score, which were relatively lower than those of the model in this study. This difference may be attributed to the level of learning between the models, and it is thought that sufficient learning would not lead to significant differences in using the existing CNN model for the detection of pulmonary emphysema.
2. The accuracy and loss for pulmonary emphysema detection of the low-dose chest CT test dataset were 92.35% and 0.21% for VGG16 and 95.88% and 0.09% for VGG19, respectively. The results of the confusion matrix and evaluation indices for the VGG16 and VGG19 models were as follows: precision, 91.
In this study, VGGNet based on CNN, which has been recently used in medical imaging, underwent learning to detect pulmonary emphysema. The usefulness of the models to diagnose pulmonary emphysema from CT images was assessed. Herein, VGG19 showed superior results than those of VGG16 in all the evaluation indices, except for recall of pulmonary emphysema.
This is because VGG19 had a relatively small ratio of False and a high ratio of True when evaluating the model. When comprehensively looking at the evaluation index of this study for the purpose of detecting that the ratio of True should be high, VGG19 with a high ratio of True is considered to be more clinically meaningful. Particularly, the F1-score was greater in VGG19 than in VGG16.

후속연구

In this study, the detection rate required for follow-up management was verified. Future studies in combination with the findings of previous studies would be useful for the diagnosis and analysis of pulmonary emphysema using low-dose chest CT images.
In this study, VGGNet was useful in detecting pulmonary emphysema using low-dose chest CT images. The findings of this study may be used as basic data for studies on customized models for detecting pulmonary emphysema.

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