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arxiv: 2405.17520 · v4 · pith:A4XLIKJM · submitted 2024-05-27 · eess.IV · cs.CV

Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images

Syed Javed , Tariq M. Khan , Abdul Qayyum , Hamid Alinejad-Rokny , Arcot Sowmya , Imran Razzak This is my paper

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classification eess.IV cs.CV
keywords medicalsegmentationimagesmini-netlightweightmethodsvariousabnormalities
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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.

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