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

Sensors

Luis Jimenez-Roldan¹  Alfonso Lagares¹  Alberto Martín²  Miguel Chavarrías²  Manuel Villa²  Guillermo Vázquez²  Eduardo Juárez²  Gemma Urbanos²  Marta Villanueva²  César Sanz² 

[1] Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain;Research Center on Software Technologies and Multimedia Systems (CITSEM), Universidad Politécnica de Madrid (UPM), Campus Sur UPM, 28031 Madrid, Spain;

关键词: hyperspectral imaging;    machine learning;    classification;    support vector machine;    random forest;    convolutional neural network;   

DOI  :  10.3390/s21113827

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown