The value of MobileNet in classification of bedside chest radiograph
10.3760/cma.j.cn112149-20230327-00216
- VernacularTitle:MobileNet对床旁胸部X线平片分类研究的价值
- Author:
Mingzhu MENG
1
;
Changjie PAN
;
Jie CHEN
;
Xiaoxia CHEN
;
Hao ZHANG
Author Information
1. 南京医科大学附属常州第二人民医院医学影像科，常州 213164
- Keywords:
Radiography;
Lightweight convolutional neural network;
Deep learning
- From:
Chinese Journal of Radiology
2023;57(12):1325-1330
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of a deep learning method based on MobileNet in classification of bedside chest radiograph and improvement of the work efficiency.Methods:A total of 6, 320 bedside chest radiographs from January 2017 to December 2022 in the Second Peoples′ Hospital of Changzhou were retrospectively collected. The included cases were divided into normal group (885 images), pneumonia group (1 927 images), pleural effusion group (373 images), and pneumonia with pleural effusion group (3 135 images). Three hundred and fifty images were selected as a validation set, while the remaining images were divided into a train set (4 775 images) and a test set (1 195 images) using simple randomization, by 8∶2 ratio. Two lightweight convolutional neural network models (MobileNetV1 and MobileNetV2) were used to construct a bedside chest radiograph classification model, based on which two fine-tuning strategies were designed. Four models were generated namely MobileNetV1_False (V1_False), MobileNetV1_True (V1_True), MobileNetV2_False (V2_False) and MobileNetV2_True (V2_True). In the first stage, a binary classification model was established to divide the images into normal and lesion groups; then a four-class classification model was established in the second stage, with which the images were divided into four groups: normal, pneumonia, pleural effusion and pneumonia with pleural effusion. Metrics for model performance evaluation including accuracy (Ac), precision (Pr), recall rate (Rc), F1 score (F1) and area under the receiver operating characteristic curve (AUC) were calculated.Results:In both the first and second stages, V1_True and V2_True had higher Ac, Pr, Rc, and F1 than V1_False and V2_False in both the training set and validation set; and the V1_True model outperformed the other three models in classification. The classification Ac of the V1_True model in the validation set was higher than that of radiologists in the first stage [95.71% (335/350) vs. 90.29% (316/350)] and in the second stage [93.43% (327/350) vs. 87.14% (305/350)]. The recognition time of V1_True model′s in the validation set of 350 bedside chest radiographs was significantly less than that of the radiologists (mean: 17 s vs. 300 min).Conclusions:V1_True is an optimal MobileNet model for classifying bedside chest radiographs. The application of this model in clinical practice may help to accurately identify the information of lung lesions from bedside chest radiographs in time, and may improve the work efficiency in the radiology department.
