Multispectral imaging and machine learning for automated cancer diagnosis

Somaya Al Maadeed, Suchithra Kunhoth, Ahmed Bouridane, Remy Peyret

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Advancing technologies in the current era paved a lot to break the hurdles in medical diagnostic field. When cancer turned out to be the most common and dangerous disease of the age, novel diagnostic methodologies were introduced to enable early detection and hence save numerous lives. Accomplishment of various automatic and semi-automatic approaches in the diagnosis has proved its sufficient impetus to improve diagnostic speed and accuracy. A wide range of image processing based tools are currently available as a part of automatic cancer detection systems. Different imaging modalities have been utilized for extracting the suspected patient information, where the multispectral imaging has emerged as an efficient means for capturing the entire range of spectral and spatial data. In this paper, we review the current multispectral imaging based methods for automatic diagnosis of major types of cancer and discuss the limitations which are yet to be overcome, so as to improve the existing systems.
Original languageEnglish
Title of host publication2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)
Place of PublicationPiscataway
PublisherIEEE
Pages1740-1744
ISBN (Print)978-1-5090-4373-6
DOIs
Publication statusE-pub ahead of print - 20 Jul 2017

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