Segmenting and Identifying Spinal Tuberculosis Disease using An Enhanced CSA and Rider Optimization Technique
Keywords:
Computed tomography, Optimization, Spinal Tuberculosis detectionAbstract
Tuberculosis disease of the vertebral causes damage to the spinal cord, which causes permanent or temporary loss of sensitivity and muscle function. Tuberculosis disease infection of the Spinal cord should be detected early and treated if the patient’s neurological condition is to improve. This paper proposes a Spinal cord segmentation and Tuberculosis disease detection method using an Enhanced Crow search –Rider optimization method to accurately diagnose Spinal Tuberculosis. First, segmentation of CT image of Spinal cord is done using Adaptive thresholding method followed by localization of disk with the help of Sparse FCM clustering algorithm. The Enhanced CSROA algorithm is used to perform classification with high accuracy. In this paper, mainly three evaluation methods have been used. It can be seen that all of them have achieved good results. Accuracy 86%, sensitivity 88% and specificity 88% have been obtained. Therefore, the proposed method proves to be an effective method for detecting spinal tuberculosis disease.
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