Enhanced pathological tissue image categorization using a bag-of-features approach with roulette wheel whale optimization

Pathological tissue image categorization is essential in medical diagnostics, offering insights into disease types, progression, and treatment alternatives. The significant variability in tissue morphology and the overlapping visual patterns across different classes complicate accurate categorization. This study introduces an improved categorization model utilizing a bag-of-features (BoF) methodology integrated with the Roulette Wheel Whale Optimization Algorithm (RWWOA) to enhance classification accuracy and optimize feature selection efficiency. The proposed model utilizes the Bag-of-Features (BoF) technique to extract discriminative features from tissue images, thereby generating a feature-rich dictionary that represents various pathological structures. The RWWOA is employed to optimize feature selection, thereby reducing dimensionality and concentrating on the most pertinent features for precise categorization. Our method integrates the exploration capabilities of the Whale Optimization Algorithm (WOA) with the probabilistic selection mechanism of the roulette wheel, thereby dynamically balancing exploitation and exploration, which enhances convergence speed and categorization accuracy. Experimental results indicate that the RWWOA-BoF method outperforms traditional methods across various datasets, showing enhancements in classification precision, recall, and F1-score. This method offers a reliable resource for aiding pathologists in diagnostic imaging, which may expedite diagnostic processes and improve consistency in clinical practice.