Hyperparameter Optimization for Brain Tumor Classification with Hyperspectral Images

Abstract

Hyperspectral (HS) imaging (HSI) techniques have demonstrated to be useful in the medical field to characterize tissues without any contact and without ionizing the patient. Besides, HSI combined with supervised machine learning (ML) algorithms have proven to be an effective technique to assist neurosurgeons to resect brain tumors. This research looks at the effects of hyperparameter optimization on two common supervised ML algorithms used for brain tumor classification: support vector machines (SVM) and random forest (RF). Correctly classifying brain tumor with HS data containing low spatial and spectral information can be challenging. To tackle this problem, this study has applied hyperparameter optimization techniques on SVM and RF with 10 brain images of patients suffering from glioblastoma multiforme (GBM) with non-mutated isocitrate dehydrogenase (IDH) enzymes. These captures have 409x217 spatial resolution and 25 normalized reflectance wavelengths gathered from 665 to 960 nm with a HS snapshot camera. Results show how this work has been able to obtain 98,60% of weighted area under the curve (AUC) on the test score by employing naive optimizations like grid search (GS) or random search (RS) and even more complex methods based on Bayesian optimization (BO). Not only the weighted AUC of SVM has been improved by 8%, but BO have also enhanced the AUC of the tumor class by 22.50% in comparison with non-optimized SVM models in the state-of-the-art, achieving AUC values of 95,49% on the tumor class. Furthermore, these improvements have been illustrated with classification maps to demonstrate the importance of hyperparameter optimization on SVM to clearly classify brain tumor, whereas non-optimized models from previous studies are unable to detect the tumor.