Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches
This work addresses the challenge of reliable polyp detection in colonoscopy for medical diagnosis, but it is incremental as it builds on existing CNN methods with specific enhancements.
The study tackled automatic colon polyp detection by applying a region-based CNN with transfer learning and image augmentation, and proposed post-learning methods to reduce false positives, achieving better performance than existing systems.
Automatic detection of colonic polyps is still an unsolved problem due to the large variation of polyps in terms of shape, texture, size, and color, and the existence of various polyp-like mimics during colonoscopy. In this study, we apply a recent region based convolutional neural network (CNN) approach for the automatic detection of polyps in images and videos obtained from colonoscopy examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning scheme in the detection system. To overcome the polyp detection obstacles and the small number of polyp images, we examine image augmentation strategies for training deep networks. We further propose two efficient post-learning methods such as, automatic false positive learning and off-line learning, both of which can be incorporated with the region based detection system for reliable polyp detection. Using the large size of colonoscopy databases, experimental results demonstrate that the suggested detection systems show better performance compared to other systems in the literature. Furthermore, we show improved detection performance using the proposed post-learning schemes for colonoscopy videos.