Multispectral imaging and machine learning for automated cancer diagnosis

Al-Maadeed, Somaya, Kunhoth, Suchithra, Bouridane, Ahmed and Peyret, Remy (2017) Multispectral imaging and machine learning for automated cancer diagnosis. In: 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, Piscataway, pp. 1740-1744. ISBN 978-1-5090-4373-6

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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.

Item Type: Book Section
Uncontrolled Keywords: Cancer detection, automatic, multispectral, hyperspectral, infrared imaging
Subjects: B800 Medical Technology
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 05 Sep 2017 10:03
Last Modified: 08 Sep 2020 15:27

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