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<p>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. <!--Query ID="Q1" Text="Kindly check and confirm the edit made in the title. " Resolved="yes"-->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)&#xa0;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).<!--Query ID="Q2" Text="Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary." Resolved="yes"--></p>

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

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Bhagyashri Thakare,
  • Bhushan Chaudhari,
  • Madhuri Patil,
  • Sachin Kamble

摘要

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).