GDN：一种用于皮肤癌诊断的堆叠网络
GDN: A Stacking Network Used for Skin Cancer Diagnosis
AI总结：
本文提出堆叠网络GDN，先并行训练GoogLeNet和DenseNet进行皮肤癌基础分类，再用逻辑回归融合二者输出，从而提升基底细胞癌和黑色素瘤的识别准确率。
摘要：
皮肤癌是可以通过视觉识别来确认的主要癌症类型，因此需要一种能够准确分类不同类型病变的自动识别系统。本文提出了GoogLe-Dense网络（GDN），这是一种图像分类模型，用于识别两种类型的皮肤癌：基底细胞癌和黑色素瘤。GDN利用不同网络的堆叠来提升模型性能。具体而言，GDN在结构上由两个顺序层级组成。第一层级执行由GoogLeNet和DenseNet完成的基本分类任务，二者并行训练以提高效率。为了避免低准确率和长训练时间，第二层级将GoogLeNet和DenseNet的输出作为逻辑回归模型的输入。我们在数据集上将所提方法与四种基线网络（包括ResNet、VGGNet、DenseNet和GoogLeNet）进行了比较，其中GoogLeNet和DenseNet显著优于ResNet和VGGNet。在第二层级中，研究了不同的堆叠方法，如感知机、逻辑回归、支持向量机、决策树和K近邻，其中逻辑回归在所有方法中表现出最佳的预测结果。结果表明，与单一网络结构相比，GDN在优化皮肤癌检测方面具有更高的准确率。
英文摘要：
Skin cancer, the primary type of cancer that can be identified by visual recognition, requires an automatic identification system that can accurately classify different types of lesions. This paper presents GoogLe-Dense Network (GDN), which is an image-classification model to identify two types of skin cancer, Basal Cell Carcinoma, and Melanoma. GDN uses stacking of different networks to enhance the model performance. Specifically, GDN consists of two sequential levels in its structure. The first level performs basic classification tasks accomplished by GoogLeNet and DenseNet, which are trained in parallel to enhance efficiency. To avoid low accuracy and long training time, the second level takes the output of the GoogLeNet and DenseNet as the input for a logistic regression model. We compare our method with four baseline networks including ResNet, VGGNet, DenseNet, and GoogLeNet on the dataset, in which GoogLeNet and DenseNet significantly outperform ResNet and VGGNet. In the second level, different stacking methods such as perceptron, logistic regression, SVM, decision trees and K-neighbor are studied in which Logistic Regression shows the best prediction result among all. The results prove that GDN, compared to a single network structure, has higher accuracy in optimizing skin cancer detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
