A Survey of Machine Learning Techniques Leveraging Brightness Indicators for Image Analysis in Biomedical Applications

Abstract

This paper presents a comprehensive survey of machine-learning techniques that leverage brightness indicators for image analysis within biomedical applications. By examining commonalities and challenges in brightness-based analysis, this survey provides insights into machine learning (ML) methods that enhance interpretability, noise reduction, and feature detection in the biomedical field. We explore how brightness indicators aid in medical image segmentation, classification, and enhancement, analysing methods that improve diagnostic accuracy and efficiency. By categorising recent techniques, this survey highlights the strengths and limitations of each method and current open research questions, as well as promising directions for integrating brightness-based ML approaches in clinical and research settings.