Enhancing Prostate Cancer Classification by Leveraging Key Radiomics Features and Using the Fine-Tuned Linear SVM Algorithm

Enhancing Prostate Cancer Classification by Leveraging Key Radiomics Features and Using the Fine-Tuned Linear SVM Algorithm

This paper focuses on enhancing machine learning (ML)-based diagnosis and clinical decision-making by leveraging radiomics data, which provides a quantitative description of grayscale medical images such as MRI, CT, PET, or X-Ray. Extracted using advanced mathematical and statistical analysis method...

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Bibliographic Details
Main Authors: Metin Varan, Jahongir Azimjonov, Bilgen Macal
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Radiomics feature extraction
efficient radiomics features
random search-based hyper parameter tuning
fine-tuned linear SVM classifier
fine-tuned ridge regressor
feature selection algorithms
Online Access:https://ieeexplore.ieee.org/document/10224245/

Internet

https://ieeexplore.ieee.org/document/10224245/

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