解码皮肤癌分类:透过研究人员的镜头的观点,见解和进展
Amartya Ray1, Sujan Sarkar1, Friedhelm Schwenker2
1Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India.
Scientific reports
|December 18, 2024
概括
这项调查审查了107篇关于人工智能 (AI) 用于皮肤癌分类的论文. 它强调了深度学习的进步,为研究人员和临床医生提供了洞察力,以提高诊断准确度.
科学领域:
- 皮肤病学和医学成像学
- 计算机视觉和人工智能的人工智能
背景情况:
- 皮肤癌是全球主要的健康问题,需要准确的诊断.
- 传统的诊断方法有其局限性.
- 计算机辅助诊断 (CAD) 系统正在改变皮肤病学.
研究的目的:
- 18年来,调查基于人工智能的皮肤癌分类方面的进展.
- 评估各种分类技术,重点是计算机视觉和人工智能.
- 弥合人工智能研究和临床皮肤病学实践之间的差距.
主要方法:
- 对107篇关于皮肤癌分类的研究论文的分析.
- 对HAM10000和ISIC档案等数据集的审查.
- 检查传统的机器学习,深度学习 (CNNs,ViTs,GANs),混合和多式联网方法.
主要成果:
- 包括CNN,转移学习,注意力机制和视觉转换器在内的深度学习方法显示出显著的进展.
- 混合和多式联络技术提供了更高的分类准确性.
- 批判性地分析了各种方法和常见数据集的局限性.
结论:
- 人工智能,特别是深度学习,大大提高了皮肤癌分类准确度.
- 该调查提供了对当前趋势,差距和临床医生人工智能工具的实际整合的见解.
- 需要进一步的研究来改进人工智能模型,以改善诊断决策.
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