Vol.62, No.3, 2020, pp.1187-1200, doi:10.32604/cmc.2020.05883
OPEN ACCESS
RESEARCH ARTICLE
Classification and Research of Skin Lesions Based on Machine Learning
  • Jian Liu1, Wantao Wang1, Jie Chen2, *, Guozhong Sun3, Alan Yang4
1 School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
2 Department of Breast Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China.
3 Dawning Information Industry Chengdu Co., Ltd., Chengdu, 610213, China.
4 Amphenol AssembleTech, Houston, TX 77070, US.
* Corresponding Author: Jie Chen. Email: chenjiedoctor@126.com.
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.
Keywords
Skin lesions, deep learning, data expansion, ensemble.
Cite This Article
Liu, J., Wang, W., Chen, J., Sun, G., Yang, A. (2020). Classification and Research of Skin Lesions Based on Machine Learning. CMC-Computers, Materials & Continua, 62(3), 1187–1200.