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The current research challenge is to build algorithms that are output-focused and adhere to the trend of quality assurance. Inter- and intra-behavioral changes in the system have an impact on the output quality. This is a result of the class imbalance problem (CIP) that exists in the data sets and is caused by the reading of unknown values. When data sets are in such a state, predictive models that rely on them become severely unbalanced. As selection of samples and candidates for the algorithms is the first step in the CIP on uncertain data, the chosen random samples predict a high degree of class variation, a very small class, or perhaps a class that is much larger than anticipated. The semantic segmentation of images can be accomplished using a dynamic weighting method that is based on the effective sample idea. The results show that this weighting approach can improve the minimal-class segmentation accuracy while guaranteeing that the segmentation performance overall in multi-class segmentation tasks is verified in two separate semantic segmentation tasks.

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A Dynamic Effective Class Balanced Approach for Semantic Segmentation of Imbalanced Medical Image Data

  • 会议论文

  • 发表日期：

    2025年

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  • Sreedevi Chikkudu,
  • Konkala Divya,
  • A. Jyothi Babu,
  • Ranjith Kumar Painam,
  • K. Reddy Madhavi,
  • V. Mahalakshmi

摘要

The current research challenge is to build algorithms that are output-focused and adhere to the trend of quality assurance. Inter- and intra-behavioral changes in the system have an impact on the output quality. This is a result of the class imbalance problem (CIP) that exists in the data sets and is caused by the reading of unknown values. When data sets are in such a state, predictive models that rely on them become severely unbalanced. As selection of samples and candidates for the algorithms is the first step in the CIP on uncertain data, the chosen random samples predict a high degree of class variation, a very small class, or perhaps a class that is much larger than anticipated. The semantic segmentation of images can be accomplished using a dynamic weighting method that is based on the effective sample idea. The results show that this weighting approach can improve the minimal-class segmentation accuracy while guaranteeing that the segmentation performance overall in multi-class segmentation tasks is verified in two separate semantic segmentation tasks.