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

Accurate colorectal polyp segmentation is crucial for the early detection and prevention of colorectal cancer, one of the leading causes of cancer-related deaths worldwide. While colonoscopy remains the most reliable screening method, it is time-consuming, resource-intensive, and highly dependent on the operator, which can lead to variability in diagnosis and potential delays. Deep learning models have shown great potential in automating polyp detection, but their large size and high computational demands make them impractical for real-time clinical use. To overcome these challenges, we introduce LPD-Net, a lightweight and efficient alternative to DUCK-Net that reduces computational complexity while maintaining high segmentation accuracy. This is achieved by optimizing the network architecture, reducing the number of residual blocks, and leveraging depthwise and pointwise convolutions. Our model strikes a balance between performance and computational efficiency. With robust preprocessing and test-time augmentation, LPD-Net achieves state-of-the-art segmentation on CVC-ClinicDB and Kvasir-SEG while remaining lightweight.Clinical RelevanceEarly and precise polyp segmentation is essential for effective colorectal cancer screening and treatment. LPD-Net ensures high segmentation accuracy while significantly reducing parameters, enabling real-time analysis of colonoscopy images. Its lightweight design lowers computational costs, making it suitable for resource-limited settings. By enhancing segmentation efficiency and robustness, LPD-Net supports faster and more reliable polyp assessment, aiding timely medical intervention and improved patient outcomes.