ImRecUnet : Colon Polyp Segmentation Based on Adaptive Improved Recalling U‐Net Context

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Abstract
Colon polyp segmentation is important in the early detection of colorectal cancer. Advances in artificial intelligence and computer‐aided diagnosis systems have dramatically improved the capabilities of colonoscopy by enhancing the segmentation and detection of colonic polyps. Accurate segmentation is essential, as it allows for the differentiation of polyps from surrounding tissues and aids in assessing their potential malignancy. This paper proposed a method for segmenting colon polyp images by improving the Recalling U‐Net model based on the original U‐Net and U‐Net++ models. The new model aims to take full advantage of multiscale features by introducing full‐scale skip connections, combining low‐level details with high‐level semantics from full‐scale feature maps, but with fewer parameters. The model can develop deep supervision to learn hierarchical representations from full‐scale aggregated feature maps in order to optimize the combined loss function to enhance organ boundaries. Specifically, the proposed method improves four major changes compared to Recalling U‐Net: separate down sampling process, improved encoding path, upgraded bridge and improved recalling block layers. These changes help improve the learning ability and efficiency of the proposed method, resulting in more accurate segmentation results. The proposed method is tested on the Kvasir‐SEG, CVC‐ClinicDB and CVC‐ColonDB datasets with evaluation metrics such as Dice coefficient, Intersection over Union and accuracy. Experimental results presented that the proposed method achieved an accuracy of 98.992%, 97.628%, and 97.695% on these datasets, respectively, and outperforms the results of several recent methods on the same dataset.

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