Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images
<|reference_start|>Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images: Accurate segmentation of anatomical structures and abnormalities in medical images is crucial for computer-aided diagnosis and analysis. While deep learning techniques excel at this task, their computational demands pose challenges. Additionally, some cutting-edge segmentation methods, though effective for general object segmentation, may not be optimised for medical images. To address these issues, we propose Mini-Net, a lightweight segmentation network specifically designed for medical images. With fewer than 38,000 parameters, Mini-Net efficiently captures both high- and low-frequency features, enabling real-time applications in various medical imaging scenarios. We evaluate Mini-Net on various datasets, including DRIVE, STARE, ISIC-2016, ISIC-2018, and MoNuSeg, demonstrating its robustness and good performance compared to state-of-the-art methods.<|reference_end|>
@article{javed2024advancing,
    title={Advancing Medical Image Segmentation with Mini-Net: A Lightweight
  Solution Tailored for Efficient Segmentation of Medical Images},
    author={Syed Javed, Tariq M. Khan, Abdul Qayyum, Hamid Alinejad-Rokny, Arcot
  Sowmya, Imran Razzak},
    journal={arXiv preprint arXiv:2405.17520},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17520},
    primaryClass={eess.IV cs.CV}
}