Authors - Sanjeeb Prasad Panday, Ujawal Thapa, Basanta Joshi, Aman Shakya, Anunaya Pandey Abstract - Early diagnosis of colorectal diseases depends upon the detection of polyps in colonoscopy images. These polyps often blend into their surrounding which often poses a challenge in detecting them. In this regard, we introduce a new approach that improves polyp segmentation using distraction mining. Our method is based on the enhancement of Positioning and Focus Network (PFNet) which was originally designed for camouflaged object segmentation. The PFNet first identifies potential polyp regions using the Positioning Module (PM) and then refines the detection by focusing on hard-to-distinguish areas using the Focus Module (FM). We integrate a distraction mining technique into FM which helps the model differentiate polyps from misleading background details and further improved the accuracy. The comparison of the PFNet model with other models like SINet and PRANet. The PFNet models and other models like SINet and PRANet are evaluated on a different polyp datasets like Colon DB, Laribpolyp DB, and CVC-300. The result shows that the distraction mining enhance the segmentation performance on a complex datasets like laribpolyp DB with 0.8046 for S-measure, 0.6651 for weighted F-measure,0.0202 for MAE,0.8590 for adaptive E-measure, and CVC-300 with 0.8220 for S-measure, 0.7317 for weighted F-measure, 0.0299 for MAE and 0.8735 for adaptive Emeasures. There are slightly low accuracy in the colon DB datasets.
