ShSCN-Net: SHEPHERD SIAMESE CONVOLUTIONAL NETWORK FOR SPINAL CORD INJURY LEVEL CLASSIFICATION

Spinal Cord Injury (SCI) detection is crucial in medical imaging for early analysis and effective therapy planning. This paper emphasizes the role of deep learning (DL) in enhancing diagnostic efficiency. This paper introduces an innovative hybrid DL model for classifying SCI levels. The proposed model, called the Shepherd Siamese Convolutional Network (ShSCN-Net), combines the Siamese Convolutional Neural Network (SCNN) and the Shepherd Convolutional Neural Network (ShCNN). First, the Computed Tomography (CT) image obtained from a database is given as the input to the image denoising phase, where the denoising of the images is done by using a median filter. Then the denoised images are segmented using a Multi-scale Attention Network (MANet). Later, using the active contour model, the disc localization of the spinal cord image is done. Subsequently, feature extraction is done to cut down the magnitude of the input information for processing. Next, injury level detection is achieved through the proposed model, with the identified injuries classified into four categories: Cervical (C1–C8), Thoracic (T1–T12), Lumbar (L1–L5), and Sacral (S1–S5) through the ShSCN-Net. The proposed ShSCN-Net is evaluated for its effectiveness in determining spinal cord injuries using various metrics, including accuracy, True Positive Rate (TPR), and True Negative Rate (TNR). Across 90% of the data used for training, the ShSCN-Net achieves notable values of 91.042% for accuracy, 92.855% for TPR, and 91.644% for TNR.