Fully Conventional Generator Network for Colorectal Polyp Segmentation in Colonoscopy Images
Keywords:
Colonoscopy; Colorectal Cancer; CNN; Normal analysis.Abstract
The early stage of Colorectal cancer (CRC) prediction is useful to decrease the mortality and morbidity rate and also to increase the diagnosis efficiency of the patient-specific treatments. CRCs are treatable if the polyps are detected in the earliest stages. Colonoscopy has established itself as a useful diagnostic method for examining abnormalities in the lower digestive system and can accurately localize lesions. We propose a novel segmentation network, Fully Convolutional Generator Network(FCG-Net), for automatic localization and segmentation of polyps using colonoscopy images. The generator model based on the fully convolutional network can realize the segmentation network's end-to-end output and enrich the semantic information of polyps through transverse connections. FCG-Net can also input the segmentation prediction images and the labeled images into the discriminant convolutional network and improve the segmentation accuracy of polyps by further enhancing the essential characteristics of learning data through the confrontation training of generators and discriminators. The experimental results demonstrate that higher performance is provided by the newly developed FCGN-Net predictive model when compared to other comparative algorithms while considering the negative and positive metrics.
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