aceIShuffleNet-PCNN hybrid classifier within an MResU-Net-based framework for spinal cord injury detection and classification using CT images
Spinal cord injury (SCI) is a serious medical condition. Spinal Cord Injury limits the movement of the body, blocks the nervous system and affects the quality of life of an injured patient. Accurate detection and classification of these fractures are essential for timely diagnosis and treatment planning; however, conventional assessment methods often struggle with noise, variability, and subtle injury patterns in CT imaging.This study aimed to develop an integrated deep learning framework for accurate and robust…
An integrated framework using WBAF preprocessing, MResU-Net segmentation, IPHOG feature extraction and IShuffleNet-PCNN classification achieved 0.933 accuracy and 0.991 NPV for spinal cord injury-related fracture classification from CT images.
Evidence
- Peer-reviewedEuropean Spine Journal2026-08-17
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Truvace Impact Record TRV-2026-0826, v1: “IShuffleNet-PCNN hybrid classifier within an MResU-Net-based framework for spinal cord injury detection and classification using CT images.” Truvace, 2026-08-18. /record/TRV-2026-0826 (accessed at citation time). sha256 d9088095b99d3515…
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