发表机构
Universidade Tecnológica Federal do Paraná (UTFPR); Universidade Federal do Vale do São Francisco (Univasf)（巴拉那联邦理工大学; 圣弗朗西斯科河谷联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究系统比较了五种预训练CNN在皮肤镜与组织病理学图像上的黑色素瘤分类性能，发现模型性能因成像模态而异，为临床选择合适架构提供了依据。
AI 中文摘要
黑色素瘤的早期诊断对于提高患者生存率至关重要。然而，由于病变类型之间高度的视觉相似性以及图像采集条件的变异性，准确区分黑色素瘤与其他皮肤病变仍然是一个重大的临床挑战。人工智能，尤其是机器学习，已成为通过自动化医学图像特征提取来支持皮肤病学诊断的有前景工具。在现有方法中，卷积神经网络（CNNs）在图像分类任务中表现出强大的性能，使其非常适合分析皮肤镜和组织病理学图像，因为它们能够捕获与病变表征相关的层次化视觉模式。然而，尽管已提出了众多预训练CNN架构，但针对特定成像模态选择最合适的架构仍然是一个开放性问题。在本研究中，我们使用皮肤镜和组织病理学图像数据集评估了用于皮肤病变分类的预训练卷积神经网络（CNNs）。实验在HAM10000、ISIC 2018和CR-AI4SkIN数据集上进行，在相同的训练协议下评估了ResNet50、VGG16、VGG19、MobileNet和InceptionV3架构。实验评估显示，在皮肤镜图像上，模型取得的准确率范围从71%（InceptionV3在ISIC 2018上）到84%（ResNet50在HAM10000上）。对于组织病理学图像，在CR-AI4SkIN数据集上准确率范围从72%（VGG19）到83%（ResNet50）。结果表明，模型性能在皮肤镜和组织病理学图像模态之间存在差异，显示在皮肤镜图像上表现相似的架构在组织病理学数据上表现出不同的性能。
英文摘要
Early diagnosis of melanoma is critical for improving patient survival rates. However, accurately distinguishing melanoma from other skin lesions remains a significant clinical challenge due to the high visual similarity among lesion types and variability in image acquisition conditions. Artificial intelligence, particularly machine learning, has emerged as a promising tool to support dermatological diagnosis by automating feature extraction from medical images. Among the available approaches, convolutional neural networks (CNNs) have demonstrated strong performance in image classification tasks, making them well-suited for analyzing both dermatoscopic and histopathological images, given their ability to capture hierarchical visual patterns relevant to lesion characterization. Nevertheless, despite numerous pre-trained CNN architectures having been proposed, selecting the most appropriate one for a given imaging modality remains an open challenge. In this study, we evaluate pre-trained convolutional neural networks (CNNs) for skin lesion classification using dermatoscopic and histopathological image datasets. Experiments were conducted on the HAM10000, ISIC 2018, and CR-AI4SkIN datasets, evaluating the ResNet50, VGG16, VGG19, MobileNet, and InceptionV3 architectures under the same training protocol. The experimental evaluation showed that the models achieved accuracies ranging from 71% (InceptionV3 on ISIC 2018) to 84% (ResNet50 on HAM10000) on dermatoscopic images. For histopathological images, accuracies ranged from 72% (VGG19) to 83% (ResNet50) on the CR-AI4SkIN dataset. The results demonstrate that model performance differs between dermatoscopic and histopathological image modalities, showing that architectures exhibiting similar performance on dermatoscopic images exhibit different performance on histopathological data.
