Abstract
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.
Highlights
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 promising domain
For the last couple of decades in particular, modern29 imaging techniques such as X-rays, magnetic resonance30 imaging (MRI), and ultrasound have had significant impacts31 on medical symptoms analysis and on the32 spawning of more imaging techniques for improvised33 examination
Neural Networks are a category of learning algorithms built upon the idea and structure of a human brain, as the name suggests, and it lays the foundation for the majority of the deep learning methods
Summary
Information and is obtained from multiple input images [4]. Medical imaging refers to images used to aid in clinical work relative to the human body such as surgical procedures, diagnoses for impeding diseases, or to analyze and study body functions and is primarily based on radiological research. ML algorithms make use of data and statistical models to learn and identify patterns to complete specific tasks and make decisions with or without human supervision Several such ML algorithms are utilized when examination of hyperspectral images is considered and in identifying and classifying differences in a tissue specimen when studying MHSI. For certain situations in which published work corresponded with multiple papers, we only considered papers with greater significance in regard to their contributions The goal for these papers being included in this survey, as mentioned earlier, encompassed the benefits of deep learning methodologies, how they are contributing towards medical hyperspectral imaging, and the challenges being faced for effectively applying these deep learning techniques to medical hyperspectral imaging. We will delve upon various deep learning methods, which are built upon the aforementioned-ML fundamentals
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