NDLI: Visual saliency based global–local feature representation for skin cancer classification

Content Provider	IET Digital Library
Author	Xiao, Feng Wu, Qiuxia
Abstract	With the rapid increase in the cases of deadly skin cancer, the classification on different types of skin cancer has been emerging as one of the most significant issues in the field of medical image. Several approaches have been proposed to help in diagnosing the categories of the skin lesions by means of traditional features or leveraging the widely used deep learning models. However, there are lack of the integrated frameworks to combine the hand-crafted traditional features and the deep Conv-features. Furthermore, the effective way to extract global and local features is also conducive to distinguish the specific lesions from normal skin. Hence, in this study, the authors present an integrated model to acquire more representative global–local features including the traditional local binary pattern features and deep Conv-features. In addition, several fusion strategies have conducted on the Global-DNN and Local-DNN for better performance. In order to extract more explicit features from the specific lesion areas, a target segmentation method based on visual saliency detection is employed to eliminate the background interference. Experimental results on ISIC-2017 skin cancer dataset demonstrate that the proposed Global-DNN and Global-Local models can obtain more effective feature representation which achieve outperformed results for skin cancer classification.
Starting Page	2140
Ending Page	2148
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 10, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1018
Journal	IET Image Processing
Publisher Date	2020-04-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Measurement And Imaging Cancer Computer Vision And Image Processing Technique Deadly Skin Cancer Deep Conv-features Deep Learning Model Effective Feature Representation Explicit Feature Feature Extraction Global Feature Global-DNN Global-Local Model Global–local Feature Representation Hand-crafted Traditional Feature Health Physics Image Classification Image Recognition Image Segmentation Integrated Frameworks ISIC-2017 Skin Cancer Dataset Knowledge Engineering Technique Learning in AI Medical And Biomedical Uses of Field Medical Image Processing Neural Computing Technique Neural Nets Normal Skin Object Detection Patient Diagnostic Method And Instrumentation Radiations Radioactivity Representative Global–local Features Skin Skin Cancer Classification Skin Lesions Specific Lesion Areas Specific Lesions Traditional Local Binary Pattern Feature Visual Saliency Detection
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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