A generative adversarial network to Reinhard stain normalization for histopathology image analysis

Histopathology image analysis is paramount importance for accurate diagnosing diseases and gaining insight into tissue properties. The significant challenge of staining variability continues. This research work presents a new method that merges deep learning with Reinhardstain normalization, aiming to revolutionize histopathology image analysis. The multi-data stream attention-based generative adversarial network is an innovative architecture designed to enhance histopathological image analysis by integrating multiple data streams, attention mechanisms, and generative adversarial networks for improved feature extraction and image quality. Multi-data stream attention-based generative adversarial network capitalizes on attention mechanisms and generative adversarial networks to process multi-modal data efficiently, enhancing feature extraction and ensuring robust performance even in the presence of staining variations. This approach excels in exact disease detection and classification, emerging as an invaluable tool for both clinical diagnoses and research endeavors across diverse datasets. The obtained accuracy of the proposed method for the SCAN dataset is 97.75%, the BACH dataset is 99.50% and the Break His dataset is 99.66%. The proposed method significantly advances histopathology image analysis, offering improved diagnostic accuracy and deeper insights by integrating multi-data streams, attention mechanisms, and generative adversarial networks. This innovative approach enhances feature extraction, image quality, and overall effectiveness in medical image analysis.