论文标题
在计算机断层扫描成像上的颈椎骨折检测的深层学习
Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging
论文作者
论文摘要
颈椎的骨折是医疗紧急情况，可能导致永久性瘫痪甚至死亡。通过计算机断层扫描（CT）怀疑骨折患者的准确诊断对于患者管理至关重要。在本文中，我们提出了一个深卷积神经网络（DCNN），其中具有双向长期术语记忆（BLSTM）层，以自动检测CT轴向图像中颈椎断裂的自动检测。我们使用了3,666张CT扫描（729个正和2,937个阴性病例）的注释数据集来训练和验证该模型。验证结果表明，平衡（104个正和104个阴性病例）和不平衡（104个阳性和419个阴性病例）测试数据集的分类精度为70.92％和79.18％。
Fractures of the cervical spine are a medical emergency and may lead to permanent paralysis and even death. Accurate diagnosis in patients with suspected fractures by computed tomography (CT) is critical to patient management. In this paper, we propose a deep convolutional neural network (DCNN) with a bidirectional long-short term memory (BLSTM) layer for the automated detection of cervical spine fractures in CT axial images. We used an annotated dataset of 3,666 CT scans (729 positive and 2,937 negative cases) to train and validate the model. The validation results show a classification accuracy of 70.92% and 79.18% on the balanced (104 positive and 104 negative cases) and imbalanced (104 positive and 419 negative cases) test datasets, respectively.
