Trends in deep learning for medical hyperspectral image analysis
This paper addresses the lack of a dedicated review for researchers and practitioners interested in the intersection of deep learning and medical hyperspectral imaging.
This review paper examines the application of deep learning algorithms for medical hyperspectral image analysis, specifically focusing on their use in classification, segmentation, and detection tasks. It aims to provide a comprehensive overview of current implementations and discuss relevant deep learning concepts within this domain.
Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this review paper aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery. This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.