Naguib Soaad M, Hamza Hanaa M, Hosny Khalid M, Saleh Mohammad K, Kassem Mohamed A
Department of Information Systems, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt.
Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt.
Diagnostics (Basel). 2023 Mar 28;13(7):1273. doi: 10.3390/diagnostics13071273.
Cervical spine (CS) fractures or dislocations are medical emergencies that may lead to more serious consequences, such as significant functional disability, permanent paralysis, or even death. Therefore, diagnosing CS injuries should be conducted urgently without any delay. This paper proposes an accurate computer-aided-diagnosis system based on deep learning (AlexNet and GoogleNet) for classifying CS injuries as fractures or dislocations. The proposed system aims to support physicians in diagnosing CS injuries, especially in emergency services. We trained the model on a dataset containing 2009 X-ray images (530 CS dislocation, 772 CS fractures, and 707 normal images). The results show 99.56%, 99.33%, 99.67%, and 99.33% for accuracy, sensitivity, specificity, and precision, respectively. Finally, the saliency map has been used to measure the spatial support of a specific class inside an image. This work targets both research and clinical purposes. The designed software could be installed on the imaging devices where the CS images are captured. Then, the captured CS image is used as an input image where the designed code makes a clinical decision in emergencies.
颈椎（CS）骨折或脱位是医疗急症，可能会导致更严重的后果，如严重的功能残疾、永久性瘫痪甚至死亡。因此，应立即对颈椎损伤进行诊断，不容有任何延误。本文提出了一种基于深度学习（AlexNet和GoogleNet）的精确计算机辅助诊断系统，用于将颈椎损伤分类为骨折或脱位。该系统旨在辅助医生诊断颈椎损伤，尤其是在急救服务中。我们在一个包含2009张X射线图像（530张颈椎脱位、772张颈椎骨折和707张正常图像）的数据集上训练了该模型。结果显示，准确率、灵敏度、特异性和精确率分别为99.56%、99.33%、99.67%和99.33%。最后，显著性图被用于测量图像中特定类别的空间支持度。这项工作兼顾了研究和临床目的。所设计的软件可以安装在采集颈椎图像的成像设备上。然后，将采集到的颈椎图像用作输入图像，由设计的代码在紧急情况下做出临床决策。
