Open Access
ARTICLE
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: .
Computers, Materials & Continua 2020, 62(2), 801-816. https://doi.org/10.32604/cmc.2020.07039
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.
Keywords
Cite This Article
F. Liu, J. Yan, W. Wang, J. Liu, J. Li
et al., "Scalable skin lesion multi-classification recognition system,"
Computers, Materials & Continua, vol. 62, no.2, pp. 801–816, 2020. https://doi.org/10.32604/cmc.2020.07039
Citations