Comprehensive study comparing different machine learning methods in computed tomography imaging

doi:10.35711/aimi.v6.i1.101264

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Jun 8, 2025 (publication date) through May 10, 2026

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Artificial Intelligence in Medical Imaging

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Artif Intell Med Imaging. Jun 8, 2025; 6(1): 101264
Published online Jun 8, 2025. doi: 10.35711/aimi.v6.i1.101264

Comprehensive study comparing different machine learning methods in computed tomography imaging

Mustafa Erdem Sağsöz

Mustafa Erdem Sağsöz, Department of Biophysics, Ataturk University, Erzurum 25050, Türkiye

Author contributions: Sağsöz ME was responsible for the concept, design, and writing of the paper.

Conflict-of-interest statement: The author reports no relevant conflicts of interest for this article.

Corresponding author: Mustafa Erdem Sağsöz, PhD, Associate Professor, Department of Biophysics, Ataturk University, Erzurum 25050, Türkiye. mesagsoz@atauni.edu.tr

Received: September 9, 2024
Revised: April 3, 2025
Accepted: April 15, 2025
Published online: June 8, 2025
Processing time: 269 Days and 21.5 Hours

Core Tip

Core Tip: This review is about the article written by Zhao et al. This study compares different machine learning methods in computed tomography imaging. In this study support vector machines, a vector space-based machine learning algorithm that finds a decision boundary between the two classes that are farthest from any point in the training data, was found to be the most effective model in the arterial and venous phases.

Sağsöz ME. Comprehensive study comparing different machine learning methods in computed tomography imaging. Artif Intell Med Imaging 2025; 6(1): 101264 [DOI: 10.35711/aimi.v6.i1.101264]

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