Transfer Learning of The ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning.
Transfer Learning of The ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning.
复制标题
用于诊断 CT 扫描中颅内出血的 ResNet-18 和 DenseNet-121 模型的迁移学习。
作者：
Qi Zhou;Wenjie Zhu;Fuchen Li;M. Yuan;Linfeng Zheng;Xu Liu
目的
验证深度学习模型在颅内出血的非增强CT中识别五种亚型和正常图像的能力。
方法
共351名患者选择39例正常组，312例颅内出血组患者行颅内出血CT平扫，共2768张图像（正常组514张图像，硬膜外出血组398张图像，硬膜下出血组501张图像，脑室内出血组497张图像，脑实质出血组415张图像，蛛网膜下腔出血组443张图像）。根据两位拥有10年以上经验的放射科医生的诊断报告，选择了ResNet-18和DenseNet-121深度学习模型。使用迁移学习。80%的数据用于训练模型，10%用于验证模型性能是否过拟合，最后10%用于模型的最终评估。评估指标包括准确性、灵敏度、特异性和AUC值。
结果
ResNet-18和DenseNet-121模型的总体准确率分别为89.64%和82.5%。鉴别五种亚型与正常图像的敏感性和特异性均在0.80以上。DenseNet-121模型识别脑室内出血和脑实质出血的灵敏度分别低于0.80、0.73和0.76。两个深度学习模型的AUC值都在0.9以上。
结论
深度学习模型可以准确识别颅内出血的五种亚型和正常图像，未来可以作为临床诊断的新工具。
OBJECTIVE
To verify the ability of the deep learning model in identifying five subtypes and normal images in noncontrast enhancement CT of intracranial hemorrhage.
METHOD
A total of 351 patients (39 patients in the normal group, 312 patients in the intracranial hemorrhage group) performed with intracranial hemorrhage noncontrast enhanced CT were selected, with 2768 images in total (514 images for the normal group, 398 images for the epidural hemorrhage group, 501 images for the subdural hemorrhage group, 497 images for the intraventricular hemorrhage group, 415 images for the cerebral parenchymal hemorrhage group, and 443 images for the subarachnoid hemorrhage group). Based on the diagnostic reports of two radiologists with more than 10 years of experience, the ResNet-18 and DenseNet-121 deep learning models were selected. Transfer learning was used. 80% of the data was used for training models, 10% was used for validating model performance against overfitting, and the last 10% was used for the final evaluation of the model. Assessment indicators included accuracy, sensitivity, specificity, and AUC values.
RESULTS
The overall accuracy of ResNet-18 and DenseNet-121 models were 89.64% and 82.5%, respectively. The sensitivity and specificity of identifying five subtypes and normal images were above 0.80. The sensitivity of DenseNet-121 model to recognize intraventricular hemorrhage and cerebral parenchymal hemorrhage was lower than 0.80, 0.73, and 0.76 respectively. The AUC values of the two deep learning models were above 0.9.
CONCLUSION
The deep learning model can accurately identify the five subtypes of intracranial hemorrhage and normal images, and it can be used as a new tool for clinical diagnosis in the future.