A serial image analysis architecture with positron emission tomography using machine learning combined for the detection of lung cancer.
Journal:
Revista espanola de medicina nuclear e imagen molecular
Published Date:
Apr 16, 2024
Abstract
INTRODUCTION AND OBJECTIVES: Lung cancer is the second type of cancer with the second highest incidence rate and the first with the highest mortality rate in the world. Machine learning through the analysis of imaging tests such as positron emission tomography/computed tomography (PET/CT) has become a fundamental tool for the early and accurate detection of cancer. The objective of this study was to propose an image analysis architecture (PET/CT) ordered in phases through the application of ensemble or combined machine learning methods for the early detection of lung cancer by analyzing PET/CT images.