An Efficient optimal threshold-based segmentation and classification model for multi-level spinal cord Injury detection

Abstract

Detection of spinal cord injury (SCI) is one of the major problems in children and adults due to variation in shape and orientation. As the types of spinal cord injuriesare increasing, it is difficult to find and predict the new type of disorder due to high dimensionality and sparsity problems. Most of the existing models are used to extract either the limited number of features or over segmented features on the SCI data. These models are not applicable to filter the essential features space with less segmented regions for injury disorder prediction. In such a scenario, we propose a hybrid threshold-based image segmentation and classification model is implemented for disorder prediction. In this model, a hybrid Ostu's thresholding method and expectation maximization (EM) approach and robust decision tree classifier are used to filter the essential features for disorder prediction. A hybrid CNN framework is used to extract the feature sets on the segmented features. Finally, a probabilistic classification model is used to predict the disease severity on the segmented image features. Experimental results illustrate the efficiency of proposed disorder prediction model with the existing models with 0.97 accuracy and 0.98 precision rate on the SCI dataset.