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Polyp segmentation is crucial for preventing colorectal cancer a common typeof cancer. Deep learning has been used to segment polyps automatically, whichreduces the risk of misdiagnosis. Localizing small polyps in colonoscopy imagesis challenging because of its complex characteristics, such as color,occlusion, and various shapes of polyps. To address this challenge, a novelfrequency-based fully convolutional neural network, Multi-Frequency FeatureFusion Polyp Segmentation Network (M3FPolypSegNet) was proposed to decomposethe input image into low/high/full-frequency components to use thecharacteristics of each component. We used three independent multi-frequencyencoders to map multiple input images into a high-dimensional feature space. Inthe Frequency-ASPP Scalable Attention Module (F-ASPP SAM), ASPP was appliedbetween each frequency component to preserve scale information. Subsequently,scalable attention was applied to emphasize polyp regions in a high-dimensionalfeature space. Finally, we designed three multi-task learning (i.e., region,edge, and distance) in four decoder blocks to learn the structuralcharacteristics of the region. The proposed model outperformed varioussegmentation models with performance gains of 6.92% and 7.52% on average forall metrics on CVC-ClinicDB and BKAI-IGH-NeoPolyp, respectively.

M3FPolypSegNet: Segmentation Network with Multi-frequency Feature Fusion for Polyp Localization in Colonoscopy Images