Exploring Transfer Learning for Deep Learning Polyp Detection in Colonoscopy Images Using YOLOv8
This work addresses polyp detection in colonoscopy images, which is incremental as it applies existing transfer learning methods to a specific medical domain.
The study tackled the challenge of limited training data for deep learning in polyp detection by investigating transfer learning with YOLOv8n, finding that pre-training on relevant datasets consistently outperformed training from scratch.
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.