Application of machine learning classifiers to X-ray diffraction imaging with medically relevant phantoms.

ABSTRACT:

Purpose

Recent studies have demonstrated the ability to rapidly produce large field of view X-ray diffraction (XRD) images, which provide rich new data relevant to the understanding and analysis of disease. However, work has only just begun on developing algorithms that maximize the performance toward decision-making and diagnostic tasks. In this study, we present the implementation of and comparison between rules-based and machine learning (ML) classifiers on XRD images of medically relevant phantoms to explore the potential for increased classification performance.

Methods

Medically relevant phantoms were utilized to provide well-characterized ground-truths for comparing classifier performance. Water and polylactic acid (PLA) plastic were used as surrogates for cancerous a

SUBMITTER: Stryker S 

PROVIDER: S-EPMC8758543 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature