|
KMID : 0357520220450010041
|
|
Journal of Radiological Science and Technology
2022 Volume.45 No. 1 p.41 ~ p.47
|
|
|
Application of Deep Learning-Based Nuclear Medicine Lung Study Classification Model
|
|
Jeong Eui-Hwan
Oh Joo-Young
Lee Ju-Young
Park Hoon-Hee
|
|
|
Abstract
|
|
|
The purpose of this study is to apply a deep learning model that can distinguish lung perfusion and lung ventilation images in nuclear medicine, and to evaluate the image classification ability. Image data pre-processing was performed in the following order: image matrix size adjustment, min-max normalization, image center position adjustment, train/validation/test data set classification, and data augmentation. The convolutional neural network(CNN) structures of VGG-16, ResNet-18, Inception-ResNet-v2, and SE-ResNeXt-101 were used. For classification model evaluation, performance evaluation index of classification model, class activation map(CAM), and statistical image evaluation method were applied.
As for the performance evaluation index of the classification model, SE-ResNeXt-101 and Inception-ResNet-v2 showed the highest performance with the same results. As a result of CAM, cardiac and right lung regions were highly activated in lung perfusion, and upper lung and neck regions were highly activated in lung ventilation. Statistical image evaluation showed a meaningful difference between SE-ResNeXt-101 and Inception-ResNet-v2.
As a result of the study, the applicability of the CNN model for lung scintigraphy classification was confirmed. In the future, it is expected that it will be used as basic data for research on new artificial intelligence models and will help stable image management in clinical practice.
|
|
|
KEYWORD
|
|
|
Convolutional neural network, Deep learning, Lung scintigraphy, Class activation map, Nuclear medicine
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
|
|