Application Value of Deep Learning in Diagnosis of Cervical Spondylosis

Cervical spondylosis is due to cervical discs and intervertebral joints and their secondary changes to the adjacent spinal cord, nerve roots, and vertebrae Artery and other tissue structure, causing the corresponding clinical symptoms. Cervical spondylosis is a common and frequent disease among middle-aged and elderly people. The incidence of cervical spondylosis is 10 percent in people over 30 years of age and is higher among those who work at a desk.

Two kinds of cervical spondylosis are common: cervical spondylotic radiculopathy and cervical spondylotic myelopathy. The principles of treatment differ greatly, and the prognosis after treatment is also different. Precise preoperative imaging and clinical diagnosis are helpful to accurately estimate the effect of surgery and the prognosis of patients.

Therefore, to achieve early accurate diagnosis is to improve the cervical spine Key to the curative effect of the disease. Generally referred to as deep learning is a kind of pattern analysis method, through the combination of simple and nonlinear module for multi-level "character learning" or "said learning" model of each module to each layer (original input) data into a higher dimension, more abstract representation, when there are enough of these transformations, The initial "low level" feature representation can be transformed into "high level" feature representation, and complex learning tasks such as classification can be completed.

For the classification task, the features that are important to the classification are amplified by increasing the number of layers, and the irrelevant features are ignored.

At present, the main research methods include Convolutional Neural Networks (CNN), self-coding Neural Networks based on multi-layer neurons, and deep confidence Networks. At present, CNN is mainly used in the field of medical image recognition.