GDN: A Stacking Network Used for Skin Cancer Diagnosis
Jingmin Wei Haoyang Shen Ziyi Wang Ziqian Zhang
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.