使用机器学习技术进行皮肤损伤分类和检测:系统性审查
1Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia.
Diagnostics (Basel, Switzerland)
|October 14, 2023
概括
本调查回顾了最近的机器学习和计算机视觉方法用于皮肤病变分析,涵盖分类,细分和检测. 它强调了皮肤病学研究的进展,挑战和未来方向,以早期发现疾病.
科学领域:
- 皮肤病学和医学成像学
- 医疗保健中的人工智能
背景情况:
- 皮肤病变分析对于诊断皮肤疾病至关重要.
- 传统方法在准确性和效率方面面临挑战.
- 计算机视觉和机器学习的进步提供了新的可能性.
研究的目的:
- 提供最近基于学习的皮肤病变分析方法的全面审查.
- 检查皮肤病变分类,细分和检测的技术.
- 确定当前的趋势,挑战和未来的研究方向.
主要方法:
- 系统审查最先进的研究论文.
- 分析深度学习和传统的机器学习技术.
- 检查细分算法 (基于深度学习,基于图形,基于区域).
主要成果:
- 详细审查使用各种图像格式的皮肤病变分类方法.
- 探索细分和检测技术,以精确识别损伤边界.
- 讨论关键数据集,挑战和现场评估指标.
结论:
- 机器学习显著提高了皮肤病变分析能力.
- 准确的分类,细分和检测对于改善患者的治疗结果至关重要.
- 需要进一步的研究来应对现有的挑战,并探索未来的方向.
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