Evidence map›Paper›PMID 41081850›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Improved deep joint model with tuned improved LeNet for spinal cord segmentation and injury detection using CT images.

Bhuvana S, Parthasarathy G, Balaji G N, Balaji N

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Bhuvana SSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, 632014, Vellore, India.
Parthasarathy GSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, 632014, Vellore, India. parthasarathee.g@gmail.com.
Balaji G NSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, 632014, Vellore, India.
Balaji NSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, 632014, Vellore, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spinal cord injury (SCI) is a serious global health issue that often leads to temporary or permanent loss of sensation and motor function. Early and accurate detection is crucial for improving patient outcomes. This study proposes a novel SCI detection model using CT images. The workflow includes preprocessing with Gaussian filtering for noise reduction and Z-score normalization for intensity adjustment. A Modified Deep Joint (MDJ) segmentation approach is then used to accurately segment spinal cord regions, followed by the extraction of connectivity, SLBT, color, statistical, and improved correlation features. For classification, an enhanced LeNet model is optimized using a new WaOI - ZOA algorithm. Experimental results show that the proposed WaOI - ZOA + M-LeNet model achieves a maximum accuracy of 0.9652, outperforming other models such as WaOI - ZOA + Squeeze Net (0.8641), WaOI - ZOA + LeNet (0.8564), and WaOI - ZOA + DCNN (0.7569). These findings confirm the effectiveness of the proposed approach for precise SCI detection.

Indexed as

Deep LearningImage Processing, Computer-AssistedSpinal CordSpinal Cord InjuriesTomography, X-Ray ComputedAlgorithmsConvolutional Neural NetworksHumansGaussian filteringImproved correlationImproved LeNetModified deep jointSpinal cord injury

Identifiers

PMID41081850

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.