Abstract
Early detection of malignant skin lesions is critical for improving patient outcomes in aggressive, metastatic skin cancers. This study evaluates a comprehensive system for preliminary skin lesion assessment that combines the clinically established ABCD rule of dermoscopy (analyzing Asymmetry, Borders, Color, and Dermoscopic Structures) with machine learning classification. Using a 1,000-image subset of the HAM10000 dataset, the system implements an automated, rule-based pipeline to compute a Total Dermoscopy Score (TDS) for each lesion. This handcrafted approach is compared against various machine learning solutions, including traditional classifiers (Logistic Regression, Random Forest, and SVM) and deep learning models. While the rule-based system provides high clinical interpretability, results indicate a performance bottleneck when reducing complex morphology to five numerical features. Experimental findings show that transfer learning with EfficientNet-B0 failed significantly due to domain shift between natural and medical images. In contrast, a custom three-layer Convolutional Neural Network (CNN) trained from scratch achieved 78.5% accuracy and 86.5% recall on median-filtered images, representing a 19-point accuracy improvement over traditional methods. The results demonstrate that direct pixel-level learning captures diagnostic patterns beyond handcrafted features and that purpose-built lightweight architectures can outperform large pretrained models for small, domain-specific medical datasets.
Abstract (translated)
早期发现恶性皮肤病变对于改善侵袭性和转移性皮肤癌患者的预后至关重要。本研究评估了一种综合系统，用于初步皮肤病变的筛查，该系统结合了临床公认的皮肤病学ABC德摩斯科检查规则（分析不对称性、边界、颜色和皮肤病结构）与机器学习分类方法。利用HAM10000数据集的一个包含1,000张图像的子集，该系统实施了一个自动化的基于规则的工作流程来计算每个病变的总皮肤镜评分（TDS）。这种方法被对比了各种机器学习解决方案，包括传统的分类器（逻辑回归、随机森林和支持向量机）和深度学习模型。尽管基于规则的方法提供了高临床可解释性，但结果表明在将复杂形态简化为五个数值特征时存在性能瓶颈。 实验发现表明，在自然图像和医学图像之间进行领域转换使得使用EfficientNet-B0的迁移学习失败。相比之下，一个定制的三层卷积神经网络（CNN），从零开始训练，在中值滤波后的图像上达到了78.5%的准确率和86.5%的召回率，比传统方法在准确性方面提高了19个百分点。这些结果表明直接基于像素的学习能够捕捉到超越手工制作特征的诊断模式，并且为小规模、领域特定的医学数据集量身定制的轻量级架构可以胜过大型预训练模型。 总的来说，这项研究强调了结合临床专业知识和机器学习技术在提高皮肤病变检测准确性方面的重要性。
URL
https://arxiv.org/abs/2601.15539
