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Scalable Skin Lesion Multi-Classification Recognition System

Fan Liu1, Jianwei Yan2, Wantao Wang2, Jian Liu2, *, Junying Li3, Alan Yang4

1 The First Clinical Medical College, Nanchang University, Nanchang, 330031, China.
2 School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
3 Gu’an County People’s Hospital, Langfang, 065000, China.
4 Amphenol AssembleTech, Houston, TX 77070, US.

* Corresponding Author: Jian Liu. Email: email.

Computers, Materials & Continua 2020, 62(2), 801-816.


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.


Cite This Article

APA Style
Liu, F., Yan, J., Wang, W., Liu, J., Li, J. et al. (2020). Scalable skin lesion multi-classification recognition system. Computers, Materials & Continua, 62(2), 801-816.
Vancouver Style
Liu F, Yan J, Wang W, Liu J, Li J, Yang A. Scalable skin lesion multi-classification recognition system. Comput Mater Contin. 2020;62(2):801-816
IEEE Style
F. Liu, J. Yan, W. Wang, J. Liu, J. Li, and A. Yang "Scalable Skin Lesion Multi-Classification Recognition System," Comput. Mater. Contin., vol. 62, no. 2, pp. 801-816. 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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