Evaluation of Hand-Crafted Features with Mask Images Obtained from PanNuke Dataset Using Bayesian Optimization and Machine Learning Models

Semantic image segmentation enables computing systems to understand the semantic patterns of image pixels by using deep learning models to classify the pixels into specific labels. The deep-learning models' performance in image classification has been evaluated by comparing the predicted images using deep-learned features with human-labeled images or mask images. However, there remains a substantial need to investigate the performance of machine learning models that do not use deep-learned features but use hand-crafted features. In this paper, we perform a comprehensive evaluation of the performance of the eight machine learning models using 46 hand-crafted features extracted from the PanNuke dataset including 5,179 hematoxylin and eosin images with 161,739 cell nuclei, by optimizing feature selection through Bayesian optimization. The evaluation results indicate that the ensemble learning-based models achieve higher performance compared to others across precision, recall, f1-score, and accuracy.