AttDenseUnet : Segmentation of Polyps from Colonoscopic Images Based on Attention-DenseNet-Unet Architecture

Globally, colorectal cancer is the primary cause of cancer-related death. Colonoscopy is currently one of the most common ways to identify precancerous gastrointestinal disorders. Thus, early and precise polyp segmentation is of therapeutic importance in reducing the risk of developing cancer. The manual examination is a tedious and time-consuming procedure for physicians. Many computer algorithms have been created by scientists to automatically identify problems from colonoscopic images. In order to further increase polyp segmentation performance, we provide in this study an attDenseU-Net design that concurrently includes the attention mechanism and U-Net. AttDenseU-Net reduces the amount of background in an input image while emphasizing key components by inserting a sequence of attention units in between relevant downs amp ling and upsampling operations. Integrating DenseNet blocks into U-Net architecture helps to more effective and efficient feature learning, and improved gradient flow. A publicly available dataset called K vasir-SEG was utilized in this study to evaluate and confirm the proposed approach. Our model's accuracy rate is 86.31 %, its Dice coefficient is 91.48%, and its Jaccard index is 84.30 %. The experiment findings show that the proposed AttDenseU-Net outperforms its baselines and provide s performance on par with existing polyp segmentation methods.