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Segmented Glioma Classification Using Radiomics-Based Machine Learning: A Comparative Analysis of Feature Selection Techniques

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Agents and Artificial Intelligence (ICAART 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14546))

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Abstract

Accurate classification of glioma grades is crucial for effective treatment planning and patient prognosis. In this extended study, we propose a comprehensive approach combining radiomics features and machine learning techniques to classify glioma grades. We explore the effectiveness of different feature selection methods, including Recursive Feature Elimination (RFE), Minimum Redundancy - Maximum Relevance (MRMR), and k-best, in identifying relevant features from segmented glioma for accurate classification. To achieve this, a deep learning approach that combines Convolutional Neural Networks (CNN) based on the hybridization of U-Net and SegNet is investigated in this study.

The evaluation of the proposed approach involves training and testing machine learning models, including Linear Regression, Random Forest and XGBoost, using the selected features from each feature selection technique. The obtained results show that XGBoost with k-best feature selection achieves the highest accuracy and Area Under the Curve (AUC) for distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). This indicates the effectiveness of the k-best feature selection method in capturing the most discriminative information for glioma grade classification. To the best of our knowledge, this is the first study to incorporate machine learning with RFE, MRMR, and k-best feature selection methods for predicting glioma grade. The proposed approach demonstrates improved accuracy compared to existing methods, highlighting the potential of radiomics and machine learning in glioma classification.

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Author information

Authors and Affiliations

  1. Université de Tunis El Manar, Institut Supérieur d’Informatique, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de Recherche en Informatique, Modélisation et Traitement de l’Information et de la Connaissance (LIMTIC), 2 Rue Abou Rayhane Bayrouni, 2080, Ariana, Tunisia

    Amal Jlassi, Khaoula ElBedoui & Walid Barhoumi

  2. Ecole Nationale d’Ingénieurs de Tunis (Enit), LR RISC, Tunis, Tunisia

    Amel Omri

  3. Université de Carthage, Ecole Nationale d’Ingénieurs de Carthage, 45 Rue des Entrepreneurs, 2035, Tunis-Carthage, Tunisia

    Khaoula ElBedoui & Walid Barhoumi

Authors
  1. Amal Jlassi
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  2. Amel Omri
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  3. Khaoula ElBedoui
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  4. Walid Barhoumi
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Corresponding author

Correspondence to Amal Jlassi .

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Editors and Affiliations

  1. LIACC, University of Porto, Porto, Portugal

    Ana Paula Rocha

  2. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

  3. Leiden Institute of Advanced Computer Science, Leiden, The Netherlands

    Jaap van den Herik

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Jlassi, A., Omri, A., ElBedoui, K., Barhoumi, W. (2024). Segmented Glioma Classification Using Radiomics-Based Machine Learning: A Comparative Analysis of Feature Selection Techniques. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2023. Lecture Notes in Computer Science(), vol 14546. Springer, Cham. https://doi.org/10.1007/978-3-031-55326-4_21

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  • DOI: https://doi.org/10.1007/978-3-031-55326-4_21

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