@Article{cmc.2020.05883,
AUTHOR = {Jian Liu, Wantao Wang, Jie Chen, Guozhong Sun, Alan Yang},
TITLE = {Classification and Research of Skin Lesions Based on Machine Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {3},
PAGES = {1187--1200},
URL = {http://www.techscience.com/cmc/v62n3/38348},
ISSN = {1546-2226},
ABSTRACT = {Classification of skin lesions is a complex identification challenge. Due to the 
wide variety of skin lesions, doctors need to spend a lot of time and effort to judge the 
lesion image which zoomed through the dermatoscopy. The diagnosis which the 
algorithm of identifying pathological images assists doctors gets more and more attention. 
With the development of deep learning, the field of image recognition has made longterm progress. The effect of recognizing images through convolutional neural network 
models is better than traditional image recognition technology. In this work, we try to 
classify seven kinds of lesion images by various models and methods of deep learning, 
common models of convolutional neural network in the field of image classification 
include ResNet, DenseNet and SENet, etc. We use a fine-tuning model with a multi-layer 
perceptron, by training the skin lesion model, in the validation set and test set we use data 
expansion based on multiple cropping, and use five models’ ensemble as the final results. 
The experimental results show that the program has good results in improving the 
sensitivity of skin lesion diagnosis.},
DOI = {10.32604/cmc.2020.05883}
}