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

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.

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Publication Details

Journal
Tomography
Published
2024-12-26
DOI
https://doi.org/10.3390/tomography11010001
Primary Topic
Medical Imaging and Analysis
Type
article
Field-Weighted Citation Impact
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article

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

Nesrin Aydın Atasoy, Yasemin Sarı
Tomography
Medical Imaging and Analysis
article

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

Nesrin Aydın Atasoy, Yasemin Sarı
article en

Abstract

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.

TomographyVol. 11(1)
Karabük University (TR)
Openalex Percentile: Top 77%
Medical Imaging and Analysis
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Enhanced Disc Herniation Classification Using Grey Wolf Optimization Based on Hybrid Feature Extraction and Deep Learning Methods — Nesrin Aydın Atasoy, Yasemin Sarı · Tomography (2024) | TGRS Research Map | TGRS