Multimodal Imaging-assisted Diagnosis Model for Cervical Spine Tumors

Cervical spine tumor is a small-sample tumor disease with low incidence, great harm, and complex anatomical structure. It is very difficult to identify and classify benign and malignant cervical spine tumors clinically. The deep learning model we constructed in the early stage is suitable for the large number of cases. The imaging diagnosis of cervical spondylosis has a high accuracy rate and a good clinical application effect, but the accuracy rate is low for cervical spine tumors with a small number of cases. The reason may be that for tasks with limited amount of data, the traditional deep learning model is difficult to play an effective role. Based on this, we propose to construct a small sample-oriented deep learning model to assist clinicians in the diagnosis of cervical spine tumors in multi-modal imaging, and to evaluate the benign and malignant tumors. This research will not only improve the efficiency and efficiency of cervical spine tumor imaging diagnosis. Accuracy, to guide clinical personalized treatment, will also provide a basis for the clinical application of deep learning in the field of small samples, which has important clinical significance.