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A Hybrid CNN-Based Segmentation and Boosting Classifier for Real Time Sensor Spinal Cord Injury Data

Convolutional Neural network (CNN) based spinal cord disease prediction has emerged as a reliable model in medical imaging applications. Spinal cord injury (SCI) detection is one of the major problems for disorder segmentation and classification. Traditionally, radiologists analyze SCI images manually in order to detect abnormal spinal disorders. Manual interpretation of high dimensional feature space makes it difficult to predict the exact category and level of severity. On the other hand, deep learning framework helps to diagnose accurately and quickly. Deep learning approach is used to classify normal abnormal SCI images automatically. This paper presents a deep learning framework for helping diagnose SCI features based on the segmentation process. In this paper, a novel CNN-deep segmentation based boosting classifier is applied on sensor SCI image data. A real-time wearable sensor is used to capture the spinal cord disorder data with different shapes and orientations. Experimental results show that the present CNN-deep segmentation based boosting classifier has high computational SCI disorder prediction compared to the existing CNN based classifiers. Experimental results proved that the present model has better performance than the existing spinal cord injury detection models in terms of true positive rate; TP = 0.9859, Accuracy = 0.9894, and Error rate = 0.019 are concerned.