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对黑色素瘤病变的分类探索皮肤镜结构
Fiza Saeed Malik1, Muhammad Haroon Yousaf1,2, Hassan Ahmed Sial3
1Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan.
Frontiers in big data
|April 9, 2024
概括
这项研究通过使用人工智能来分析皮肤镜结构来增强黑色素瘤检测,提高准确性并解决人工智能模型脆弱性,以便更好地诊断皮肤癌.
科学领域:
- 皮肤病学 皮肤病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 黑色素瘤是一种危险的皮肤癌,在早期发现的情况下,其治愈率很高,但错误分类显著降低了生存率.
- 黑色素瘤和良性瘤的临床变异给皮肤科医生带来了诊断挑战.
- 目前的诊断方法有局限性,这突出了皮肤病学中人工智能的需要.
研究的目的:
- 使用人工智能探索皮肤镜结构,以改进黑色素瘤病变的分类.
- 调查和解决人工智能模型在区分黑色素瘤和良性病变中的"脆弱性".
- 为了提高AI模型对黑色素瘤诊断中的图像变异的稳定性.
主要方法:
- 使用了专家注释的皮肤镜像数据集.
- 使用变压器和卷积神经网络 (CNN) 模型进行图像分类.
- 专注于关键的皮肤镜结构:蓝白面,点/球体和条纹.
- 评估模型性能和易受图像变化的敏感性,使用各种测试集.
主要成果:
- 增加卷积的视觉变压器在黑色素瘤分类中达到高达98%的准确性.
- 美国CNN (VGG-16,DenseNet-121) 显示50-60%的准确性,在非皮肤镜特征方面表现更好.
- 没有卷积的视觉变压器表现出脆弱性,在各种数据集上精度降低.
- 数据增强策略成功地减轻了AI模型的脆弱性和持续的准确性.
结论:
- 提出了一个使用三个皮肤镜结构的新型黑色素瘤分类方案.
- 该研究涉及人工智能模型对黑色素瘤诊断中的图像变异的敏感性.
- 未来的工作包括收集更大的注释数据集和自动化皮肤镜特征提取.
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