Abstract
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.
Abstract (translated)
色素皮肤病变代表局部黑色素增多的区域，可能是包括黑素瘤在内的严重疾病的标志。黑素瘤是导致皮肤癌死亡的主要因素之一。最近，受MNIST数据集启发而创建的MedMNIST v2数据集被引入以推进生物医学影像研究，并包含基于HAM10000数据集构建的用于色素病变分类的数据子集DermaMNIST。 本研究评估了ResNet-50和EfficientNetV2L模型在使用DermaMNIST进行多类分类任务中的表现，采用了迁移学习以及多种层配置。其中一种配置达到了与现有方法相匹配或超越的结果。这项研究表明，卷积神经网络（CNN）可以推动生物医学图像分析的进步，并显著提高诊断准确性。
URL
https://arxiv.org/abs/2507.12961
