Open AccessMultiscale ensemble of convolutional neural networks for skin lesion classification
Author(s) -
Liu YiPeng,
Wang Ziming,
Li Zhanqing,
Li Jing,
Li Ting,
Chen Peng,
Liang Ronghua
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12214
Subject(s) - convolutional neural network , computer science , artificial intelligence , pattern recognition (psychology) , skin lesion , lesion , deep learning , artificial neural network , field (mathematics) , medicine , mathematics , pathology , pure mathematics
Early detection and treatment of skin cancer can considerably reduce the patient mortality rates. Convolutional neural network (CNN) has been widely applied in the field of computer aided diagnosis. However, for skin lesions, the inconsistent size of lesion regions in dermatoscope images hinders the convolutional neural network precise discrimination. To solve this problem, multiscale ensemble of convolutional neural networks called MECNN is proposed, which involves three branches with different lesion scales as the model input. The first branch locates the lesion region outline by identifying the largest local response point. Then, MECNN reduces the search area of the lesion region and divides the outline into two scales used as the input for the other two branches. A global loss function is defined to control the learning objectives of the three branches and MECNN fuses the branches output as the final classification result. The proposed model is evaluated on the public HAM10000 dataset and achieves a higher classification accuracy than the comparative state‐of‐the‐art methods.