Next Article in Journal
Infection-Mediated Clinical Biomarkers for a COVID-19 Electrical Biosensing Platform
Next Article in Special Issue
GoRG: Towards a GPU-Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications
Previous Article in Journal
Evaluation of Child–Computer Interaction Using Fitts’ Law: A Comparison between a Standard Computer Mouse and a Head Mouse
Previous Article in Special Issue
Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 21
Issue 11
10.3390/s21113827
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification

by
Gemma Urbanos
1,2,†,
Alberto Martín
1,†,
Guillermo Vázquez
1,
Marta Villanueva
1,
Manuel Villa
1,
Luis Jimenez-Roldan
2,
Miguel Chavarrías
1,*,
Alfonso Lagares
2,
Eduardo Juárez
1 and
César Sanz
1
1
Research Center on Software Technologies and Multimedia Systems (CITSEM), Campus Sur UPM, Universidad Politécnica de Madrid (UPM), 28031 Madrid, Spain
2
Instituto de Investigación Sanitaria Hospital 12 de Octubre (Imas12), 28041 Madrid, Spain
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sensors 2021, 21(11), 3827; https://doi.org/10.3390/s21113827
Submission received: 30 April 2021 / Revised: 21 May 2021 / Accepted: 26 May 2021 / Published: 31 May 2021
(This article belongs to the Special Issue Trends and Prospects in Medical Hyperspectral Imagery)
Browse Figures
Versions Notes

Abstract

Hyperspectral imaging techniques (HSI) do not require contact with patients and are non-ionizing as well as non-invasive. As a consequence, they have been extensively applied in the medical field. HSI is being combined with machine learning (ML) processes to obtain models to assist in diagnosis. In particular, the combination of these techniques has proven to be a reliable aid in the differentiation of healthy and tumor tissue during brain tumor surgery. ML algorithms such as support vector machine (SVM), random forest (RF) and convolutional neural networks (CNN) are used to make predictions and provide in-vivo visualizations that may assist neurosurgeons in being more precise, hence reducing damages to healthy tissue. In this work, thirteen in-vivo hyperspectral images from twelve different patients with high-grade gliomas (grade III and IV) have been selected to train SVM, RF and CNN classifiers. Five different classes have been defined during the experiments: healthy tissue, tumor, venous blood vessel, arterial blood vessel and dura mater. Overall accuracy (OACC) results vary from 60% to 95% depending on the training conditions. Finally, as far as the contribution of each band to the OACC is concerned, the results obtained in this work are 3.81 times greater than those reported in the literature.
Keywords: hyperspectral imaging; machine learning; classification; support vector machine; random forest; convolutional neural network; brain; neurosurgery; tumor hyperspectral imaging; machine learning; classification; support vector machine; random forest; convolutional neural network; brain; neurosurgery; tumor

Share and Cite

MDPI and ACS Style

Urbanos, G.; Martín, A.; Vázquez, G.; Villanueva, M.; Villa, M.; Jimenez-Roldan, L.; Chavarrías, M.; Lagares, A.; Juárez, E.; Sanz, C. Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification. Sensors 2021, 21, 3827. https://doi.org/10.3390/s21113827

AMA Style

Urbanos G, Martín A, Vázquez G, Villanueva M, Villa M, Jimenez-Roldan L, Chavarrías M, Lagares A, Juárez E, Sanz C. Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification. Sensors. 2021; 21(11):3827. https://doi.org/10.3390/s21113827

Chicago/Turabian Style

Urbanos, Gemma, Alberto Martín, Guillermo Vázquez, Marta Villanueva, Manuel Villa, Luis Jimenez-Roldan, Miguel Chavarrías, Alfonso Lagares, Eduardo Juárez, and César Sanz. 2021. "Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification" Sensors 21, no. 11: 3827. https://doi.org/10.3390/s21113827

APA Style

Urbanos, G., Martín, A., Vázquez, G., Villanueva, M., Villa, M., Jimenez-Roldan, L., Chavarrías, M., Lagares, A., Juárez, E., & Sanz, C. (2021). Supervised Machine Learning Methods and Hyperspectral Imaging Techniques Jointly Applied for Brain Cancer Classification. Sensors, 21(11), 3827. https://doi.org/10.3390/s21113827

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop