Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 8
2550-2555,2560

Application of SC-Net model integrated with transfer learning and data augmentation in skin cancer classification

Zuo Hangxu1
Liao Bin1
Chen Xiaokun1
Tong Yang2
Li Yong3
1. College of Statistics & Data Science, Xinjiang University of Finance & Economics, Urumqi 830012, China
2. College of Electronic information, University of Electronic Science & Technology of China, Chengdu 610000, China
3. College of Integration of Traditional Chinese & Western Medicine, Southwest Medical University, Luzhou Sichuan 646100, China
DOI: 10.19734/j.issn.1001-3695.2021.12.0666

Abstract

The performance of the current skin cancer diagnostic models cannot meet the requirements of clinical applications, and the diagnostic accuracy is not high for a few categories. To solve this problem, this paper proposed a SC-Net model based on transfer learning and data augmentation. Firstly, it used the ECA attention module to fine tune the pre-training model of DenseNet-201 on the skin cancer dataset, and extracted the implicit high-level features of the images. Then, it joined the general statistical features, and used SMOTE oversampling technology to balance a few categories of data. Finally, it putt the data into XGBoost model for training to obtain the final SC-Net classification model. The experimental results show that the accuracy, sensitivity and specificity of SC-Net model reach 99.25%, 99.25% and 99.88%, which is about 0.6%~18.7% higher than the existing models. The proposed model has stronger classification ability for a few categories such as Dermato fibroma and Actinic keratoses and intraepithelial carcinoma.

Foundation Support

国家自然科学基金资助项目(61562078,71563048)
新疆天山青年计划资助项目(2018Q073)
新疆高校研自科项目(XJEDU2021Y037)
新疆“天山雪松计划”青年拔尖人才计划项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.12.0666
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 8
Section: Technology of Graphic & Image
Pages: 2550-2555,2560
Serial Number: 1001-3695(2022)08-054-2550-06

Publish History

[2022-02-16] Accepted Paper
[2022-08-05] Printed Article

Cite This Article

左航旭, 廖彬, 陈小昆, 等. 融合迁移学习和数据增强的SC-Net模型在皮肤癌识别中的应用 [J]. 计算机应用研究, 2022, 39 (8): 2550-2555,2560. (Zuo Hangxu, Liao Bin, Chen Xiaokun, et al. Application of SC-Net model integrated with transfer learning and data augmentation in skin cancer classification [J]. Application Research of Computers, 2022, 39 (8): 2550-2555,2560. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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