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Article

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

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.
Academic Editor: Paola Saccomandi
Sensors 2021, 21(11), 3827; https://doi.org/10.3390/s21113827
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)
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. View Full-Text
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
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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

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