融合神经模糊与殖民竞争算法，提升皮肤癌早期诊断准确率
Optimizing Neuro-Fuzzy and Colonial Competition Algorithms for Skin Cancer Diagnosis in Dermatoscopic Images
- 用神经模糊与殖民竞争算法融合处理皮肤镜图像
- 在560张图像上达到94%诊断准确率
- 适合临床辅助诊断系统开发人员参考
皮肤癌发病率上升，公众认知不足与临床专家短缺凸显了先进诊断辅助工具的迫切需求。人工智能（AI）在区分恶性与良性皮肤病变方面展现出巨大潜力。基于公开皮肤病变数据集，研究者们正开发AI诊断方案。然而，这些计算机系统在临床中的应用仍处于初期阶段。本研究通过融合图像处理技术与机器学习算法，特别是神经模糊和殖民竞争方法，应用于ISIC数据库的皮肤镜图像，实现了560张图像上94%的准确率。结果表明该方法在辅助医生早期发现黑色素瘤方面具有显著潜力，对皮肤癌诊断具有重要意义。
原文摘要 · Abstract (English)
The rising incidence of skin cancer, coupled with limited public awareness and a shortfall in clinical expertise, underscores an urgent need for advanced diagnostic aids. Artificial Intelligence (AI) has emerged as a promising tool in this domain, particularly for distinguishing malignant from benign skin lesions. Leveraging publicly available datasets of skin lesions, researchers have been developing AI-based diagnostic solutions. However, the integration of such computer systems in clinical settings is still nascent. This study aims to bridge this gap by employing a fusion of image processing techniques and machine learning algorithms, specifically neuro-fuzzy and colonial competition approaches. Applied to dermoscopic images from the ISIC database, our method achieved a notable accuracy of 94% on a dataset of 560 images. These results underscore the potential of our approach in aiding clinicians in the early detection of melanoma, thereby contributing significantly to skin cancer diagnostics.
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