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This study aims to apply advanced machine learning algorithms to improve the prediction accuracy and efficiency of a skin cancer dataset. Firstly, we conducted a thorough analysis and preprocessing of the dataset to ensure the quality of the input data. Subsequently, we experimented with various machine learning models, including support vector machines, decision trees, and convolutional neural networks. The performance of each model was compared and optimized, exploring different model optimization strategies to further enhance prediction accuracy. The findings of this study provide valuable insights for utilizing machine learning techniques combined with attention mechanisms for skin cancer diagnosis and treatment.

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Research on Multi-model Comparison and Machine Learning Algorithms in Skin Cancer Aided Diagnosis

  • 会议论文

  • 发表日期：

    2026年

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  • Longqing Zhang,
  • Zishang Wang,
  • Hongming Chen,
  • Xinwei Zhang,
  • Qian Chen,
  • Yingyi Zhang,
  • Yihang Chen

摘要

This study aims to apply advanced machine learning algorithms to improve the prediction accuracy and efficiency of a skin cancer dataset. Firstly, we conducted a thorough analysis and preprocessing of the dataset to ensure the quality of the input data. Subsequently, we experimented with various machine learning models, including support vector machines, decision trees, and convolutional neural networks. The performance of each model was compared and optimized, exploring different model optimization strategies to further enhance prediction accuracy. The findings of this study provide valuable insights for utilizing machine learning techniques combined with attention mechanisms for skin cancer diagnosis and treatment.