**Samuel Ortega 1,2,\*,**†**, Martin Halicek 1,3,**†**, Himar Fabelo 2, Rafael Camacho 4, María de la Luz Plaza 4, Fred Godtliebsen 5, Gustavo M. Callicó <sup>2</sup> and Baowei Fei 1,6,7,\***


Received: 6 March 2020; Accepted: 28 March 2020; Published: 30 March 2020

**Abstract:** Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies.

**Keywords:** hyperspectral imaging; medical optics and biotechnology; optical pathology; convolutional neural networks; tissue diagnostics; tissue characterization; glioblastoma
