Polyp segmentation on colonoscopy image using improved Unet and transfer learning

Abstract

Colorectal cancer is among the most common malignancies and can develop from high-risk colon polyps. Colonoscopy remains the gold-standard investigation for colorectal cancer screening. The procedure could benefit greatly from using AI models for automatic polyp segmentation, which provide valuable insights for improving colon polyp dection. Additionally, it will support gastroenterologists during image analysation to correctly choose the treatment with less time. In this paper, the framework of polyp image segmentation is developed by a deep learning approach, especially a convolutional neural network. The proposed framework is based on improved Unet architecture to obtain the segmented polyp image. We also propose to use the transfer learning method to transfer the knowledge learned from the ImageNet general image dataset to the endoscopic image field. This framework used the Kvasir-SEG database, which contains 1000 GI polyp images and corresponding segmentation masks according to annotation by medical experts. The results confirmed that our proposed method outperform the state-of-the-art polyp segmentation methods with 94.79% dice, 90.08% IOU, 98.68% recall, and 92.07% precision.