Exploring Transfer Learning for Deep Learning Polyp Detection in Colonoscopy Images Using YOLOv8

TL;DR

This study investigates how transfer learning from various datasets affects the performance of YOLOv8n in colonoscopy polyp detection, emphasizing the importance of dataset relevance and size for optimal results.

Contribution

It systematically evaluates the impact of pre-training datasets on YOLOv8n's polyp detection performance, highlighting the benefits of domain-specific pre-training.

Findings

Pre-training on relevant datasets improves detection accuracy.

Larger, diverse datasets generally enhance transfer learning effectiveness.

Domain similarity between pre-training data and target task boosts performance.

Abstract

Deep learning methods have demonstrated strong performance in objection tasks; however, their ability to learn domain-specific applications with limited training data remains a significant challenge. Transfer learning techniques address this issue by leveraging knowledge from pre-training on related datasets, enabling faster and more efficient learning for new tasks. Finding the right dataset for pre-training can play a critical role in determining the success of transfer learning and overall model performance. In this paper, we investigate the impact of pre-training a YOLOv8n model on seven distinct datasets, evaluating their effectiveness when transferred to the task of polyp detection. We compare whether large, general-purpose datasets with diverse objects outperform niche datasets with characteristics similar to polyps. In addition, we assess the influence of the size of the dataset on the efficacy of transfer learning. Experiments on the polyp datasets show that models pre-trained on relevant datasets consistently outperform those trained from scratch, highlighting the benefit of pre-training on datasets with shared domain-specific features.