Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145335
Title: Melanoma recognition in dermoscopy images via aggregated deep convolutional features
Authors: Yu, Zhen
Jiang, Xudong
Zhou, Feng
Qin, Jing
Ni, Dong
Chen, Siping
Lei, Baiying
Wang, Tianfu
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Yu, Z., Jiang, X., Zhou, F., Qin, J., Ni, D., Chen, S., ... Wang, T. (2019). Melanoma recognition in dermoscopy images via aggregated deep convolutional features. IEEE Transactions on Biomedical Engineering, 66(4), 1006-1016. doi:10.1109/TBME.2018.2866166
Journal: IEEE Transactions on Biomedical Engineering
Abstract: In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very deep residual neural network pretrained on a large natural image dataset. Then these local deep descriptors are aggregated by orderless visual statistic features based on Fisher vector (FV) encoding to build a global image representation. Finally, the FV encoded representations are used to classify melanoma images using a support vector machine with a Chi-squared kernel. Our proposed method is capable of generating more discriminative features to deal with large variations within melanoma classes, as well as small variations between melanoma and nonmelanoma classes with limited training data. Extensive experiments are performed to demonstrate the effectiveness of our proposed method. Comparisons with state-of-the-art methods show the superiority of our method using the publicly available ISBI 2016 Skin lesion challenge dataset.
URI: https://hdl.handle.net/10356/145335
ISSN: 1558-2531
DOI: 10.1109/TBME.2018.2866166
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TBME.2018.2866166
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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