Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection.

ABSTRACT: This study compares various preprocessing techniques for hyperspectral deep learning-based cancer diagnostics. The study considers different spectrum scaling and noise reduction options across spatial and spectral axes of hyperspectral datacubes, as well varying levels of blood and light reflections removal. We also examine how the size of the patches extracted from the hyperspectral data affects the models' performance. We additionally explore various strategies to mitigate our dataset's imbalance (where cancerous tissues are underrepresented). Our results indicate that. Scaling: Standardization significantly improves both sensitivity and specificity compared to Normalization. Larger input patch sizes enhance performance by capturing more spatial context. Noise reduction unexpectedly degr

SUBMITTER: Tkachenko M 

PROVIDER: S-EPMC12457627 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature