@Article{cmc.2020.07039,
AUTHOR = {Fan Liu, Jianwei Yan, Wantao Wang, Jian Liu, Junying Li, Alan Yang},
TITLE = {Scalable Skin Lesion Multi-Classification Recognition System},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {2},
PAGES = {801--816},
URL = {http://www.techscience.com/cmc/v62n2/38277},
ISSN = {1546-2226},
ABSTRACT = {Skin lesion recognition is an important challenge in the medical field. In this 
paper, we have implemented an intelligent classification system based on convolutional 
neural network. First of all, this system can classify whether the input image is a 
dermascopic image with an accuracy of 99%. And then diagnose the dermoscopic image 
and the non-skin mirror image separately. Due to the limitation of the data, we can only 
realize the recognition of vitiligo by non-skin mirror. We propose a vitiligo recognition
based on the probability average of three structurally identical CNN models. The method 
is more efficient and robust than the traditional RGB color space-based image recognition 
method. For the dermoscopic classification model, we were able to classify 7 skin lesions, 
use weighted optimization to overcome the unbalanced data set, and greatly improve the 
sensitivity of the model by means of model fusion. The optimization and expansion of the 
system depend on the increase of database.},
DOI = {10.32604/cmc.2020.07039}
}