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Scientific photo segmentation is a crucial challenge in clinical photo evaluation as it is a critical step in growing three-D models from valuable scientific imagery for diagnosis and remedy. Convolution Neural Networks (CNNs) have been used to phase medical photographs as they proved to obtain excellent effects in phrases with cutting-edge accuracy and velocity compared to traditional techniques. We review the maximum applicable previous work, which includes Unified U-internet, Pixel Net, Multi-Scale Aggregation and attention (MSA), and Multi-Scale attention U-net (MSA-UNet). We speak success packages of those methods, analyzing the architectures followed and datasets used to guide selection-making. In the end, we provide a standard to evaluate modern-day metrics and performance standards used to evaluate the accuracy and efficiency of modern CNN processes for medical photo segmentation.

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Accurate and Efficient Medical Image Segmentation with Convolutional Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Girija Shankar Sahoo,
  • Govind Shay Sharma,
  • Vinod Mansiram Kapse,
  • Manju Bargavi

摘要

Scientific photo segmentation is a crucial challenge in clinical photo evaluation as it is a critical step in growing three-D models from valuable scientific imagery for diagnosis and remedy. Convolution Neural Networks (CNNs) have been used to phase medical photographs as they proved to obtain excellent effects in phrases with cutting-edge accuracy and velocity compared to traditional techniques. We review the maximum applicable previous work, which includes Unified U-internet, Pixel Net, Multi-Scale Aggregation and attention (MSA), and Multi-Scale attention U-net (MSA-UNet). We speak success packages of those methods, analyzing the architectures followed and datasets used to guide selection-making. In the end, we provide a standard to evaluate modern-day metrics and performance standards used to evaluate the accuracy and efficiency of modern CNN processes for medical photo segmentation.