Deep Neural Network based Polyp Segmentation in Colonoscopy Images using a Combination of Color Spaces
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Colorectal cancer (CRC) is the second leading cause of cancer death. Colorectal polyps cause most colorectal cancer cases. Colonoscopy is considered as the most common method for diagnosis of colorectal polyps, and early detection and segmentation of them can prevent colorectal cancer. On the other hand, today advances in computer systems persuade researchers all around the world to use computer-aided systems to help physicians in their diagnosis. Many modern types of researches and methods have proposed for this goal, and we have aggregated the methods based on previous convolutional neural networks with more recent networks in this paper to improve the quality of segmentation. We also chose the red channel, green channel and the b* component of CIE-L*a*b* as the input of network to leverage the parameters of segmentation result such as dice and sensitivity. LinkNet is used as the convolutional network, and the results show that it is suitable for segmentation. Performance of our method is evaluated on CVC-ColonDB. The results show that our method outperforms previous works in colorectal polyp segmentation field.
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