Automatic Identification of Brain Injury Mechanism Based on Deep Learning.
10.12116/j.issn.1004-5619.2021.410923
- Author:
Qi-Fan YANG
1
;
Xue-Yang SUN
1
;
Yan-Bin WANG
2
;
Zhi-Ling TIAN
3
;
He-Wen DONG
3
;
Lei WAN
3
;
Dong-Hua ZOU
3
;
Xiao-Tian YU
3
;
Guang-Zheng ZHANG
1
;
Ning-Guo LIU
3
Author Information
1. Department of Forensic Medicine, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450000, China.
2. China National Accreditation Service for Conformity Assessment, Beijing 100062, China.
3. Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
- Publication Type:Journal Article
- Keywords:
Inception_v3 model;
acceleration brain injury;
convolutional neural network;
deceleration brain injury;
deep learning;
forensic medicine;
image classification;
receiver operating characteristic (ROC) curve
- MeSH:
Brain/diagnostic imaging*;
Brain Injuries;
Deep Learning;
Humans;
Neural Networks, Computer
- From:
Journal of Forensic Medicine
2022;38(2):223-230
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:To apply the convolutional neural network (CNN) Inception_v3 model in automatic identification of acceleration and deceleration injury based on CT images of brain, and to explore the application prospect of deep learning technology in forensic brain injury mechanism inference.
METHODS:CT images from 190 cases with acceleration and deceleration brain injury were selected as the experimental group, and CT images from 130 normal brain cases were used as the control group. The above-mentioned 320 imaging data were divided into training validation dataset and testing dataset according to random sampling method. The model classification performance was evaluated by the accuracy rate, precision rate, recall rate, F1-value and AUC value.
RESULTS:In the training process and validation process, the accuracy rate of the model to classify acceleration injury, deceleration injury and normal brain was 99.00% and 87.21%, which met the requirements. The optimized model was used to test the data of the testing dataset, the result showed that the accuracy rate of the model in the test set was 87.18%, and the precision rate, recall rate, F1-score and AUC of the model to recognize acceleration injury were 84.38%, 90.00%, 87.10% and 0.98, respectively, to recognize deceleration injury were 86.67%, 72.22%, 78.79% and 0.92, respectively, to recognize normal brain were 88.57%, 89.86%, 89.21% and 0.93, respectively.
CONCLUSIONS:Inception_v3 model has potential application value in distinguishing acceleration and deceleration injury based on brain CT images, and is expected to become an auxiliary tool to infer the mechanism of head injury.
