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

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.

Hyperspectral imaging and machine learning in food microbiology: Developments and challenges in detection of bacterial, fungal, and viral contaminants

“…Supervised machine learning can be explained as a method that utilizes a ground truth to extract information from spectral data. Examples of supervised machine learning are SVM, k‐Nearest Neighborhood (KNN), Decision Trees, Random Forest, Linear Regression, and Neural Networks (NN) (Ozdemir & Polat, 2020; Urbanos et al., 2021). As explained in various sections of this review, the use of machine learning and chemometrics can enhance the detection efficiency and reduce the background noise to employ HSI as a routine detection method.…”

Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges

“…Recently, HI has been utilized to identify and diagnose illnesses characterized by alterations in cellular biochemical pathways (146). Urbanos et al (147) classified tumor tissue in a set of 12 HGGs using thirteen in-vivo hyperspectral photos (healthy tissue, tumor, venous blood vessel, arterial blood vessel, and dura mater). Overall accuracies for the three models (RF, SVM, and CNN) ranged from 60% to 95% depending on the training settings.…”

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

“…Fabelo, et al used a semi-automatic approach based on a SAM algorithm to define 4 distinct classes, which included normal tissue, tumor tissue, blood vessels, and background ( Fabelo et al, 2019b ). Then, Urbanos, et al used three different algorithms to classify brain tissue in vivo from 13 patients with high-grade gliomas, including support vector machines (SVM), random forests (RF), and convolutional neural networks ( Urbanos et al, 2021 ). In this work, the authors evaluated three classification algorithms with an HSI snapshot camera with limited wavelength bands and distinguished five different brain tissue types (tumor, arterial blood vessel, venous blood vessel, dura mater and healthy tissue).…”