Enhanced Disc Herniation Classification Using Grey Wolf Optimization Based on Hybrid Feature Extraction and Deep Learning Methods.

IF 2.2 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yasemin Sarı, Nesrin Aydın Atasoy
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引用次数: 0

Abstract

Due to the increasing number of people working at computers in professional settings, the incidence of lumbar disc herniation is increasing.

Background/objectives: The early diagnosis and treatment of lumbar disc herniation is much more likely to yield favorable results, allowing the hernia to be treated before it develops further. The aim of this study was to classify lumbar disc herniations in a computer-aided, fully automated manner using magnetic resonance images (MRIs).

Methods: This study presents a hybrid method integrating residual network (ResNet50), grey wolf optimization (GWO), and machine learning classifiers such as multi-layer perceptron (MLP) and support vector machine (SVM) to improve classification performance. The proposed approach begins with feature extraction using ResNet50, a deep convolutional neural network known for its robust feature representation capabilities. ResNet50's residual connections allow for effective training and high-quality feature extraction from input images. Following feature extraction, the GWO algorithm, inspired by the social hierarchy and hunting behavior of grey wolves, is employed to optimize the feature set by selecting the most relevant features. Finally, the optimized feature set is fed into machine learning classifiers (MLP and SVM) for classification. The use of various activation functions (e.g., ReLU, identity, logistic, and tanh) in MLP and various kernel functions (e.g., linear, rbf, sigmoid, and polynomial) in SVM allows for a thorough evaluation of the classifiers' performance.

Results: The proposed methodology demonstrates significant improvements in metrics such as accuracy, precision, recall, and F1 score, outperforming traditional approaches in several cases. These results highlight the effectiveness of combining deep learning-based feature extraction with optimization and machine learning classifiers.

Conclusions: Compared to other methods, such as capsule networks (CapsNet), EfficientNetB6, and DenseNet169, the proposed ResNet50-GWO-SVM approach achieved superior performance across all metrics, including accuracy, precision, recall, and F1 score, demonstrating its robustness and effectiveness in classification tasks.

基于混合特征提取和深度学习方法的灰狼优化椎间盘突出分类。
由于越来越多的人在专业环境中使用电脑工作,腰椎间盘突出症的发病率正在增加。背景/目的:腰椎间盘突出症的早期诊断和治疗更有可能产生良好的结果,使疝在进一步发展之前得到治疗。本研究的目的是利用计算机辅助、全自动的磁共振图像(mri)对腰椎间盘突出症进行分类。方法:本研究提出了一种结合残差网络(ResNet50)、灰狼优化(GWO)和多层感知器(MLP)、支持向量机(SVM)等机器学习分类器的混合方法来提高分类性能。该方法首先使用ResNet50进行特征提取,ResNet50是一种以其强大的特征表示能力而闻名的深度卷积神经网络。ResNet50的残余连接允许从输入图像中进行有效的训练和高质量的特征提取。在特征提取之后,受灰狼的社会等级和狩猎行为的启发,采用GWO算法选择相关度最高的特征对特征集进行优化。最后,将优化后的特征集输入机器学习分类器(MLP和SVM)进行分类。在MLP中使用各种激活函数(例如,ReLU、identity、logistic和tanh),在SVM中使用各种核函数(例如,线性、rbf、sigmoid和多项式),可以对分类器的性能进行全面评估。结果:提出的方法在准确性、精密度、召回率和F1分数等指标上有显著改进,在一些情况下优于传统方法。这些结果突出了将基于深度学习的特征提取与优化和机器学习分类器相结合的有效性。结论:与胶囊网络(CapsNet)、EfficientNetB6和DenseNet169等其他方法相比,本文提出的ResNet50-GWO-SVM方法在准确率、精密度、召回率和F1评分等所有指标上都取得了优异的性能,证明了其在分类任务中的鲁棒性和有效性。
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来源期刊
Tomography
Tomography Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
2.70
自引率
10.50%
发文量
222
期刊介绍: TomographyTM publishes basic (technical and pre-clinical) and clinical scientific articles which involve the advancement of imaging technologies. Tomography encompasses studies that use single or multiple imaging modalities including for example CT, US, PET, SPECT, MR and hyperpolarization technologies, as well as optical modalities (i.e. bioluminescence, photoacoustic, endomicroscopy, fiber optic imaging and optical computed tomography) in basic sciences, engineering, preclinical and clinical medicine. Tomography also welcomes studies involving exploration and refinement of contrast mechanisms and image-derived metrics within and across modalities toward the development of novel imaging probes for image-based feedback and intervention. The use of imaging in biology and medicine provides unparalleled opportunities to noninvasively interrogate tissues to obtain real-time dynamic and quantitative information required for diagnosis and response to interventions and to follow evolving pathological conditions. As multi-modal studies and the complexities of imaging technologies themselves are ever increasing to provide advanced information to scientists and clinicians. Tomography provides a unique publication venue allowing investigators the opportunity to more precisely communicate integrated findings related to the diverse and heterogeneous features associated with underlying anatomical, physiological, functional, metabolic and molecular genetic activities of normal and diseased tissue. Thus Tomography publishes peer-reviewed articles which involve the broad use of imaging of any tissue and disease type including both preclinical and clinical investigations. In addition, hardware/software along with chemical and molecular probe advances are welcome as they are deemed to significantly contribute towards the long-term goal of improving the overall impact of imaging on scientific and clinical discovery.
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