Deep Learning in Medical Hyperspectral Images: A Review

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 24; p. 9790
• Main Authors: Cui, Rong, Yu, He, Xu, Tingfa, Xing, Xiaoxue, Cao, Xiaorui, Yan, Kang, Chen, Jiexi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.12.2022; MDPI
• Subjects: Algorithms; Classification; Deep Learning; Diagnostic Imaging; Discriminant analysis; Disease; disease diagnosis; Medical diagnosis; medical hyperspectral imaging systems; Medical imaging; Medical imaging equipment; Medicine; Neural networks; Remote sensing; Review; Sensors; Spectrum analysis; Support vector machines; Tomography; United States; Texas; China; deep learning; disease diagnosis; medical hyperspectral imaging systems
• DOI: 10.3390/s22249790
• ISSN: 1424-8220

With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.