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

Authors :
Alfonso Lagares
Eduardo Juarez
César Sanz
Manuel Villa
Guillermo Vázquez
Alberto Martin
Marta Villanueva
Gemma Urbanos
M. Chavarrias
Luis Jiménez-Roldán
Source :
Sensors, Vol 21, Iss 3827, p 3827 (2021), Sensors (Basel, Switzerland), Sensors, Volume 21, Issue 11
Publication Year :
2021
Publisher :
MDPI AG, 2021.

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.

Details

Language :
English
ISSN :
14248220
Volume :
21
Issue :
3827
Database :
OpenAIRE
Journal :
Sensors
Accession number :
edsair.doi.dedup.....61d7461e1e7f67fcac547dc73d78306d