用CNN提升皮肤病变诊断准确率，效果媲美甚至超越现有方法。
Improving Diagnostic Accuracy of Pigmented Skin Lesions With CNNs: an Application on the DermaMNIST Dataset
- 基于ResNet-50与EfficientNetV2L，采用迁移学习进行分类。
- 某一配置在DermaMNIST上达到或超过已有方法的准确率。
- 适合医学图像分析、AI辅助诊断方向的研究者参考。
色素性皮肤病变是黑色素增多的局部区域，可能提示恶性黑色素瘤等严重疾病，是皮肤癌致死的主要原因。医学影像数据集MedMNIST v2受MNIST启发，旨在推动生物医学成像研究，其中DermaMNIST基于HAM10000数据集，用于色素性病变的多类别分类。本研究评估了ResNet-50与EfficientNetV2L模型在使用迁移学习和不同层结构配置下的表现。某一配置在任务中达到或超越现有方法的性能。研究表明，卷积神经网络（CNN）可显著提升生物医学图像分析中的诊断准确性，推动该领域发展。
原文摘要 · Abstract (English)
Pigmented skin lesions represent localized areas of increased melanin and can indicate serious conditions like melanoma, a major contributor to skin cancer mortality. The MedMNIST v2 dataset, inspired by MNIST, was recently introduced to advance research in biomedical imaging and includes DermaMNIST, a dataset for classifying pigmented lesions based on the HAM10000 dataset. This study assesses ResNet-50 and EfficientNetV2L models for multi-class classification using DermaMNIST, employing transfer learning and various layer configurations. One configuration achieves results that match or surpass existing methods. This study suggests that convolutional neural networks (CNNs) can drive progress in biomedical image analysis, significantly enhancing diagnostic accuracy.
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