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Medical Image Segmentation Benchmark on Kvasir‑SEG

a modified, CPU-friendly variant of the : "An Efficient Medical Segmentation Model With Edge Enhancement" doi

This repository contains a complete pipeline for training and evaluating medical image segmentation models on the Kvasir‑SEG dataset (polyp segmentation). The work includes:

  • A flexible PyTorch data loader for Kvasir‑SEG (images, masks, train/val/test splits).
  • Five classic segmentation models for benchmarking: U‑Net, SegNet, ResUNet, PraNet, and U‑Net++.
  • A lightweight implementation inspired by the state‑of‑the‑art ÆMMamba paper, capturing its core ideas:
    – Mamba‑like 1D scanning (efficient long‑range dependencies)
    – Edge‑Aware Module (Sobel operator + gated fusion)
    – Multi‑scale fusion with boundary‑sensitive decoding
  • CPU‑friendly training (no GPU required) – uses only torch, torchvision, PIL, tqdm, numpy.

📁 Dataset Structure

Download the Kvasir‑SEG dataset from Simula Dataset Portal.

Install dependencies

pip install torch torchvision pillow tqdm numpy

Run the code

python mambaseg.py

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the design, optimization, and empirical validation of a modified, CPU-friendly variant of the ÆMMamba architecture applied to the Kvasir-SEG dataset.

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