Multispectral imaging and machine learning for automated cancer diagnosis
Author | Al Maadeed, Somaya |
Author | Kunhoth, Suchithra |
Author | Bouridane, Ahmed |
Author | Peyret, Remy |
Available date | 2021-01-25T06:45:46Z |
Publication Date | 2017 |
Publication Name | 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017 |
Resource | 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. |
Sponsor | This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 6-249-1-053. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Automatic Cancer detection Hyperspectral Infrared imaging Multispectral |
Type | Conference Paper |
Pagination | 1740-1744 |
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