Comprehensive study comparing different machine learning methods in computed tomography imaging

Abstract

The article written by Zhao et al, which was recently accepted for publication, introduces an innovative method that combines deep learning-based feature extraction with a radiomics nomogram to create a noninvasive procedure for determining perineural invasion status in patients with rectal cancer. This method is an artificial intelligence application in which researchers segment their own datasets, derive features and analyze their weights. It was found that the support vector machine was the most effective model in the arterial and venous phases. A support vector machine is a machine learning algorithm based on a vector space that finds a decision boundary between the two classes furthest from any point in the training data.

Keywords: Deep learning; Perineural invasion; Radiomics; Rectal cancer; Stacking nomogram; Support vector machines

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.