An improved framework for polyp image segmentation based on SegNet architecture

A polyp is one of the major causes of gastroenterology, which leads to colorectal cancer. The detection of polyps by colonoscopy imaging is a significant challenge because of the diversity in polyp structure and lack of examination accuracy. To solve this problem, the automatic segmentation of polyps can be used to enhance examination accuracy and reduce gastrointestinal (GI) disease. In this paper, the framework of polyp image segmentation is developed by a deep learning approach, especially a convolutional neural network. This proposed framework used the Kvasir-SEG database, which contains 1000 GI polyp images and corresponding segmentation masks according to annotation by medical experts. This database is divided into 900 for training images and 100 for testing images. This framework is based on image preprocessing and two types of SegNet architecture to obtain the segmented polyp image. This paper has demonstrated state-of-the-art performance on both VGG-16, and VGG-19 networks for training and testing data to address colorectal cancer screening rates. The results confirmed that the VGG-19 model has outperformed the VGG-16 model via all evaluation parameters except sensitivity for the polyp segmentation on the Kvasir-SEG dataset. Additionally, it will support gastroenterologists during medical strategy to correctly choose the treatment with less time.

Automated summary

The paper presents a framework for polyp image segmentation using deep learning, specifically convolutional neural networks. By utilizing the Kvasir-SEG database, the framework demonstrated state-of-the-art performance in enhancing examination accuracy for gastroenterologists. The results showed that the VGG-19 model outperformed the VGG-16 model in polyp segmentation on the Kvasir-SEG dataset, providing valuable support for medical decision-making.