Research Article
ReinhardGAN Hybrid Strategic Light optimization for Normalization of H E Stained Colorectal Cancer
@ARTICLE{10.4108/eetpht.11.10445, author={Shubhajit Panda and Mahesh Jangid and Ashish Jain}, title={ReinhardGAN Hybrid Strategic Light optimization for Normalization of H E Stained Colorectal Cancer}, journal={EAI Endorsed Transactions of Pervasive Health and Technology}, volume={11}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2026}, month={5}, keywords={Arithmetic Optimization Algorithm, Hematoxylin and Eosin, Normalization, Reinhard Stain Normalization, War Strategy Optimization}, doi={10.4108/eetpht.11.10445} }- Shubhajit Panda
Mahesh Jangid
Ashish Jain
Year: 2026
ReinhardGAN Hybrid Strategic Light optimization for Normalization of H E Stained Colorectal Cancer
PHAT
EAI
DOI: 10.4108/eetpht.11.10445
Abstract
Stain normalization is a crucial pre-processing process for the accurate interpretation of Haematoxylin and Eosin (H&E) stained histopathology images based on colorectal cancer. Effective normalization improves classification accuracy by reducing computational complexity, addressing inter- variability in background colors across a dataset, and minimizing data loss. This paper proposes a novel approach that combines Reinhard normalization with a Generative Adversarial Network (ReinhardGAN) to enhance image color properties, such as consistency, contrast, and luminance. To further optimize the normalization process, a Hybrid Strategic Light Optimization (HSLO) algorithm is introduced, minimizing the loss and computational cost during the H&E-stained image normalization. The experimental results demonstrate that the proposed ReinhardGAN-HSLO provided outstanding performance over conventional color normalization methods in terms of colour consistency and normalization as indicated by qualitative and quantitative assessments
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