论文标题
基于转移学习的技术进行比较分析，用于分类黑素细胞NEVI
A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi
论文作者
论文摘要
皮肤癌是癌症的致命表现。皮肤细胞中未修复的脱氧核糖核酸（DNA）导致皮肤遗传缺陷并导致皮肤癌。为了处理致命的死亡率，加上医疗的飙升成本，早期诊断是必须的。为了应对这些挑战，研究人员为皮肤癌开发了各种快速检测工具。病变特异性标准用于区分良性皮肤癌和恶性黑色素瘤。在这项研究中，已经对五种基于转移学习的技术进行了比较分析，这些技术有可能利用黑素细胞NEVI分类。这些技术基于深度卷积神经网络（DCNN），这些神经网络已在数千个开源图像上进行了预先培训，并在许多情况下用于日常分类任务。
Skin cancer is a fatal manifestation of cancer. Unrepaired deoxyribo-nucleic acid (DNA) in skin cells, causes genetic defects in the skin and leads to skin cancer. To deal with lethal mortality rates coupled with skyrocketing costs of medical treatment, early diagnosis is mandatory. To tackle these challenges, researchers have developed a variety of rapid detection tools for skin cancer. Lesion-specific criteria are utilized to distinguish benign skin cancer from malignant melanoma. In this study, a comparative analysis has been performed on five Transfer Learning-based techniques that have the potential to be leveraged for the classification of melanocytic nevi. These techniques are based on deep convolutional neural networks (DCNNs) that have been pre-trained on thousands of open-source images and are used for day-to-day classification tasks in many instances.
