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

Gemma Urbanos^1,2, Alberto Martín¹, Guillermo Vázquez¹

¹Research Center on Software Technologies and Multimedia Systems (CITSEM), Universidad Politécnica de Madrid (UPM), Campus Sur UPM, 28031 Madrid, Spain.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This summary is machine-generated.

Hyperspectral imaging combined with machine learning aids brain tumor surgery by differentiating healthy and tumor tissues. This non-invasive approach achieved up to 95% accuracy, improving surgical precision and reducing damage to healthy areas.

Keywords:

brain classification convolutional neural network hyperspectral imaging machine learning neurosurgery random forest support vector machine tumor

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Area of Science:

Medical imaging
Machine learning
Oncology

Background:

Hyperspectral imaging (HSI) is a non-invasive, non-ionizing medical imaging technique.
Machine learning (ML) models enhance diagnostic capabilities in healthcare.
Accurate differentiation of brain tumor tissue is critical during surgery.

Purpose of the Study:

To evaluate the efficacy of HSI combined with ML for in-vivo differentiation of brain tumor tissues.
To train and compare Support Vector Machine (SVM), Random Forest (RF), and Convolutional Neural Network (CNN) classifiers.
To assess the contribution of spectral bands to classification accuracy.

Main Methods:

Utilized thirteen in-vivo hyperspectral images from twelve high-grade glioma patients.
Defined five tissue classes: healthy, tumor, venous blood vessel, arterial blood vessel, and dura mater.
Trained SVM, RF, and CNN classifiers on hyperspectral data.

Main Results:

Achieved Overall Accuracy (OACC) ranging from 60% to 95% depending on training conditions.
Demonstrated the potential of HSI and ML for real-time surgical guidance.
Spectral band contribution analysis showed significant improvement over existing literature.

Conclusions:

HSI combined with ML offers a reliable method for intraoperative brain tumor margin assessment.
The developed ML models can assist neurosurgeons in precise tissue differentiation.
This technique has the potential to improve surgical outcomes and minimize damage to healthy brain tissue.