Sarı Yasemin, Aydın Atasoy Nesrin
The Institute of Graduate Programs, Karabük University, Karabük 78050, Türkiye.
Department of Computer Engineering, Faculty of Engineering, Karabük University, Karabük 78050, Türkiye.
Tomography. 2024 Dec 26;11(1):1. doi: 10.3390/tomography11010001.
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).
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.
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.
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.
由于在专业环境中使用电脑工作的人数不断增加，腰椎间盘突出症的发病率正在上升。
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效果，使疝气在进一步发展之前得到治疗。本研究的目的是使用磁共振成像（MRI）以计算机辅助、全自动的方式对腰椎间盘突出症进行分类。
本研究提出了一种混合方法，将残差网络（ResNet50）、灰狼优化（GWO）以及多层感知器（MLP）和支持向量机（SVM）等机器学习分类器相结合，以提高分类性能。所提出的方法首先使用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的残差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，受灰狼社会等级和狩猎行为启发的GWO算法被用于通过选择最相关的特征来优化特征集。最后，将优化后的特征集输入到机器学习分类器（MLP和SVM）中进行分类。在MLP中使用各种激活函数（例如ReLU、恒等、逻辑和双曲正切）以及在SVM中使用各种核函数（例如线性、径向基函数、sigmoid和多项式）可以全面评估分类器的性能。
所提出的方法在准确率、精确率、召回率和F1分数等指标上有显著提高，在几种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。
与其他方法（如胶囊网络（CapsNet）、EfficientNetB6和DenseNet169）相比，所提出的ResNet50 - GWO - SVM方法在所有指标（包括准确率、精确率、召回率和F1分数）上都取得了卓越的性能，证明了其在分类任务中的稳健性和有效性。
