基于深度学习的颅脑损伤机制自动化鉴别

Automatic Identification of Brain Injury Mechanism Based on Deep Learning

目的应用卷积神经网络Inception_v3模型进行基于颅脑CT图像的加、减速性损伤自动鉴别,探讨深度学习技术在法医学颅脑损伤成伤机制推断中的应用前景。方法收集190例加、减速性脑损伤的影像学资料作为实验组,另选取130例正常颅脑的影像学资料作为对照。将上述320例影像学资料根据随机抽样的方法分为训练验证集和测试集。采用准确率、精确率、召回率、F1值及AUC值评估模型分类性能。结果模型在训练过程和验证过程中对3种图像(加速性损伤、减速性损伤及正常颅脑)分类的最高准确率分别为99.00%、87.21%,满足要求。使用优化后的模型对测试集数据进行测试,该模型在测试集中的三分类准确率为87.18%,识别加速性损伤的精确率、召回率、F1值及AUC值分别为84.38%、90.00%、87.10%、0.98,识别减速性损伤的各值分别为86.67%、72.22%、78.79%、0.92,识别正常颅脑的各值分别为88.57%、89.86%、89.21%、0.93。结论Inception_v3模型在基于颅脑CT图像区分加、减速性损伤中具有应用潜力,有望成为推断头部致伤方式的一种辅助工具。

Objective 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.Conclusion 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.