Med-2D SegNet: A Light Weight Deep Neural Network for Medical 2D Image Segmentation

• URL: http://arxiv.org/abs/2504.14715v1
• Date: Sun, 20 Apr 2025 19:04:43 GMT
• Title: Med-2D SegNet: A Light Weight Deep Neural Network for Medical 2D Image Segmentation
• Authors: Md. Sanaullah Chowdhury, Salauddin Tapu, Noyon Kumar Sarkar, Ferdous Bin Ali, Lameya Sabrin,
• Abstract summary: We introduce Med-2D SegNet, a novel and highly efficient segmentation architecture.<n>Med-2D SegNet achieves state-of-the-art performance across multiple benchmark datasets.<n>Central to its success is the compact Med Block, a specialized encoder design.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and efficient medical image segmentation is crucial for advancing clinical diagnostics and surgical planning, yet remains a complex challenge due to the variability in anatomical structures and the demand for low-complexity models. In this paper, we introduced Med-2D SegNet, a novel and highly efficient segmentation architecture that delivers outstanding accuracy while maintaining a minimal computational footprint. Med-2D SegNet achieves state-of-the-art performance across multiple benchmark datasets, including KVASIR-SEG, PH2, EndoVis, and GLAS, with an average Dice similarity coefficient (DSC) of 89.77% across 20 diverse datasets. Central to its success is the compact Med Block, a specialized encoder design that incorporates dimension expansion and parameter reduction, enabling precise feature extraction while keeping model parameters to a low count of just 2.07 million. Med-2D SegNet excels in cross-dataset generalization, particularly in polyp segmentation, where it was trained on KVASIR-SEG and showed strong performance on unseen datasets, demonstrating its robustness in zero-shot learning scenarios, even though we acknowledge that further improvements are possible. With top-tier performance in both binary and multi-class segmentation, Med-2D SegNet redefines the balance between accuracy and efficiency, setting a new benchmark for medical image analysis. This work paves the way for developing accessible, high-performance diagnostic tools suitable for clinical environments and resource-constrained settings, making it a step forward in the democratization of advanced medical technology.

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