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LPD-Net: A Lightweight and Efficient Deep Learning Model for Accurate Colorectal Polyp Segmentation.

Ali Tamizifa, Zahra Sobhaninia, Behzad Mirmahboub

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
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    LPD-Net offers accurate colorectal polyp segmentation for early cancer detection. This lightweight deep learning model enhances real-time analysis of colonoscopy images, improving patient outcomes.

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Colorectal cancer (CRC) is a leading cause of cancer deaths globally.
    • Accurate segmentation of colorectal polyps is vital for early CRC detection and prevention.
    • Current colonoscopy methods are operator-dependent, leading to diagnostic variability.

    Purpose of the Study:

    • To introduce LPD-Net, a lightweight and efficient deep learning model for colorectal polyp segmentation.
    • To reduce computational complexity and improve practicality for real-time clinical applications.
    • To achieve high segmentation accuracy comparable to larger models.

    Main Methods:

    • Optimized network architecture with reduced residual blocks.
    • Utilized depthwise and pointwise convolutions for efficiency.

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  • Incorporated robust preprocessing and test-time augmentation.

    • Main Results:

    • LPD-Net achieved state-of-the-art segmentation accuracy on CVC-ClinicDB and Kvasir-SEG datasets.
    • Demonstrated a significant reduction in computational complexity and model size compared to DUCK-Net.
    • Maintained high segmentation performance with a lightweight design.

    Conclusions:

    • LPD-Net provides an efficient and accurate solution for real-time colorectal polyp segmentation.
    • The model's lightweight nature makes it suitable for resource-limited clinical settings.
    • LPD-Net supports faster, more reliable polyp assessment, aiding timely medical intervention and improved patient outcomes.