MMFIL-Net: Multi-level and multi-source feature interactive lightweight network for polyp segmentation

Abstract

Accuracy, generalized performance, and model size are critical considerations for designing a real-time polyp segmentation model. However, existing techniques primarily focus on accuracy and do not consider the other two vital characteristics. This paper proposes MMFIL-Net, a novel polyp segmentation technique. As part of MMFIL-Net, the Hierarchical Multi-source Feature Interaction Module (HMFIM) comprises Multi-source Feature Interaction Blocks (MFIB). MFIB manipulates multi-level and multi-sourced features to reduce the gap between low and high-level feature maps to achieve generalized performance. Additionally, the Multiple Receptive Field Feature Interaction Block (MRFFIB) targets the issues of segmenting polyps of different sizes. Finally, Dual Source Attention Fusion Block (DSAFB) is introduced to deal with hazy boundary information for early-stage polyps detection and segmentation. The proposed model outperformed existing lightweight models in conducted evaluation on different datasets. In addition to the achieved generalized performance and higher accuracy, the proposed model presents a significant reduction in the model size than existing approaches. The proposed model only contains 6.68 million parameters and has 4.32G MACs (Multiply–Accumulate Operations), which is better than the current approaches.