ShPCFHNet: shepherd parallel convolutional forward harmonic net for spinal cord injury detection using CT images.

Abstract

Computed Tomography (CT)has gained recognition as the leading imaging method, extensively used in the diagnosis of spinal cord injuries. The reliance on CT imaging for acute care in patients with Spinal Cord Injury (SCI) has expanded rapidly. However, the diagnosis of initial clinical injury is crucial to accurately predict functional prediction, which is a difficult task for both clinicians and radiologists. To conquer this issue, an efficient model based on SCI detection is proposed, named as Shepard Parallel Convolutional Forward Harmonic Net (ShPCFHNet). The first step involves improving the CT image by applying logarithmic transformations in the enhancement phase. Spinal cord segmentation is then performed with the aid of the proposed Dual-branch UNet, whose loss function is adapted using Sensitivity-Specificity Loss (SSL). Following this, disc localization is carried out using an active contour model, and feature extraction is subsequently performed. The final step involves detecting SCI using ShPCFHNet, which combines the Shepard Convolutional Neural Network (ShCNN) and Parallel Convolutional Neural Network (PCNN) with Harmonic analysis. The proposed model achieved performance metrics of 91.397% accuracy, 92.684% True Positive Rate (TPR), and 90.366% True Negative Rate (TNR).