OBJECTIVE The aim of the study was to verify the ability of the deep learning model to identify five subtypes and normal images in non-contrast enhancement CT of intracranial hemorrhage. METHODS A total of 351 patients (39 patients in the normal group, 312 patients in the intracranial hemorrhage group) who underwent intracranial hemorrhage noncontrast enhanced CT were selected, obtaining 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 was obtained as 89.64% and 82.5%, respectively. The sensitivity and specificity of identifying five subtypes and normal images were above 0.80. The sensitivity of the 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 found to be 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.

中文翻译：

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用于在 CT 扫描中诊断颅内出血的 ResNet-18 和 DenseNet-121 模型的迁移学习。

目的：本研究旨在验证深度学习模型在颅内出血非增强CT中识别五种亚型和正常图像的能力。方法 选取351例（正常组39例，颅内出血组312例）行颅内出血平扫增强CT检查的患者，共获得2768张图像（正常组514张，硬膜外398张）。出血组、硬膜下出血组501张、脑室内出血组497张、脑实质出血组415张、蛛网膜下腔出血组443张）。根据两位具有 10 年以上经验的放射科医师的诊断报告，选择了 ResNet-18 和 DenseNet-121 深度学习模型。使用了迁移学习。80% 的数据用于训练模型，10% 用于验证模型对过拟合的性能，最后 10% 用于模型的最终评估。评估指标包括准确性、敏感性、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%。识别五种亚型和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敏感性分别低于0.80、0.73和0.76。发现两种深度学习模型的 AUC 值均高于 0.9。