Comparative Study of Machine Learning Methods to Classify Bowel Polyps

Gastrointestinal polyps are common abnormalities detected during colonoscopy screenings, and accurate classification of these polyps is essential for effective diagnosis and treatment planning. This study aims to explore machine learning techniques’ performance for classifying gastrointestinal polyps in colonoscopy video clips. A comparative analysis is conducted to evaluate the performance of seven machine learning algorithms, including three types of tree-based algorithms, three types of boosting algorithms, and two function-based algorithms. The dataset consists of 152 instances, encompassing hyperplastic, serrated, and adenoma lesions, with a total of 76 polyps. The results of this study provide insights into the effectiveness of machine learning algorithms for improving the efficiency and accuracy of gastrointestinal polyp classification, ultimately contributing to enhanced patient care and diagnostic outcomes.