An Exploratory Analysis of ML Models in CADx Design

Abstract

The goal of this study is to explore classification models that could be potentially used in Computer Aided Diagnosis (CADx). The performances of models with different feature extraction methods, feature representations, and classifier combinations are tested and analyzed. None of the models explored in this study are viable options for CADx models. The results may reveal useful information regarding future improvement of CADx models.