Skin lesion classification based on the <scp>VGG</scp>‐16 fusion residual structure
Pu Yan, Gang Wang, Jie Chen, Qingwei Tang, Heng Xu
Abstract
Abstract The analysis of skin lesion images is challenging due to the high interclass similarity and intraclass variance. Therefore, improving the ability to automatically classify based on skin lesion images is necessary to help physicians classify skin lesions. We propose a network model based on the Visual Geometry Group Network (VGG‐16) fusion residual structure for the multiclass classification of skin lesions. based on the VGG‐16 network, we simplify and improve the network structure by adding a preprocessing layer (CBRM layer) and fusing the residual structure. We also use a hair removal algorithm and perform six data augmentation operations on a small number of skin lesion images to balance the total number of the seven skin lesions in the dataset. The model was evaluated on the ISIC2018 dataset. Experiments have shown that our network model achieves good classification performance, with a test accuracy rate of 88.14% and a macroaverage of 98%.