# Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

arXiv: https://arxiv.org/abs/2402.07192 · HTML: https://mlsanity.com/paper/2402.07192/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 58 references that are in the map.
- First posted on arXiv 11 Feb 2024.

## Built on by (most cited first, 6 of 6)

- [In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection](https://mlsanity.com/paper/2402.10776/) (2024)
- [OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations](https://mlsanity.com/paper/2411.09553/) (2024)
- [Advancements in Real-Time Oncology Diagnosis: Harnessing AI and Image Fusion Techniques](https://mlsanity.com/paper/2503.11332/) (2025)
- [Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications](https://mlsanity.com/paper/2404.10631/) (2024)
- [Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications](https://mlsanity.com/paper/2503.02908/) (2025)
- [Label tree semantic losses for rich multi-class medical image segmentation](https://mlsanity.com/paper/2507.15777/) (2025)

## Abstract (arXiv)

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a noncontact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.