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Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images.

Authors
  • Iqbal, Imran1
  • Younus, Muhammad2
  • Walayat, Khuram3
  • Kakar, Mohib Ullah4
  • Ma, Jinwen5
  • 1 Department of Information and Computational Sciences, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, People's Republic of China. Electronic address: [email protected] , (China)
  • 2 State Key Laboratory of Membrane Biology and Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine and Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, People's Republic of China. Electronic address: [email protected] , (China)
  • 3 Faculty of Engineering Technology, Department of Thermal and Fluid Engineering, University of Twente, Enschede, 7500 AE, Netherlands. Electronic address: [email protected] , (Netherlands)
  • 4 Beijing Key Laboratory for Separation and Analysis in Biomedicine and Pharmaceuticals, Beijing Institute of Technology, Beijing, 100081, People's Republic of China. Electronic address: [email protected] , (China)
  • 5 Department of Information and Computational Sciences, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, People's Republic of China. Electronic address: [email protected] , (China)
Type
Published Article
Journal
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Publication Date
Dec 24, 2020
Volume
88
Pages
101843–101843
Identifiers
DOI: 10.1016/j.compmedimag.2020.101843
PMID: 33445062
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

As an analytic tool in medicine, deep learning has gained great attention and opened new ways for disease diagnosis. Recent studies validate the effectiveness of deep learning algorithms for binary classification of skin lesions (i.e., melanomas and nevi classes) with dermoscopic images. Nonetheless, those binary classification methods cannot be applied to the general clinical situation of skin cancer screening in which multi-class classification must be taken into account. The main objective of this research is to develop, implement, and calibrate an advanced deep learning model in the context of automated multi-class classification of skin lesions. The proposed Deep Convolutional Neural Network (DCNN) model is carefully designed with several layers, and multiple filter sizes, but fewer filters and parameters to improve efficacy and performance. Dermoscopic images are acquired from the International Skin Imaging Collaboration databases (ISIC-17, ISIC-18, and ISIC-19) for experiments. The experimental results of the proposed DCNN approach are presented in terms of precision, sensitivity, specificity, and other metrics. Specifically, it attains 94 % precision, 93 % sensitivity, and 91 % specificity in ISIC-17. It is demonstrated by the experimental results that this proposed DCNN approach outperforms state-of-the-art algorithms, exhibiting 0.964 area under the receiver operating characteristics (AUROC) in ISIC-17 for the classification of skin lesions and can be used to assist dermatologists in classifying skin lesions. As a result, this proposed approach provides a novel and feasible way for automating and expediting the skin lesion classification task as well as saving effort, time, and human life. Copyright © 2020 Elsevier Ltd. All rights reserved.

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