Convolutional Neural Network Algorithm with Parameterized Activation Function for Melanoma Classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Namozov, A. | - |
dc.contributor.author | Cho, Y.I. | - |
dc.date.available | 2020-02-27T12:44:11Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4406 | - |
dc.description.abstract | Melanoma is the deadliest form of skin cancer, which is considered one of the most common human malignancies in the world. Early detection of this disease can affect the result of the illness and improve the chance of surviving. The tremendous improvement of deep learning algorithms in image recognition tasks promises a great success for medical image analysis, in particular, melanoma classification for skin cancer diagnosis. Activation functions play an important role in the performance of deep neural networks for image recognition problems as well as medical image classification. In this paper, we show that a deep neural network model with adaptive piecewise linear units can achieve excellent results in melanoma recognition. Experimental results show that a convolutional neural network model with parameterized adaptive piecewise linear units outperforms the same network with different activation functions in the melanoma classification task. All experiments are performed using the data provided in International Skin Imaging Collaboration (ISIC) 2018 Skin Lesion Analysis towards Melanoma Detection. © 2018 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018 | - |
dc.subject | Chemical activation | - |
dc.subject | Computer aided diagnosis | - |
dc.subject | Convolution | - |
dc.subject | Data mining | - |
dc.subject | Deep learning | - |
dc.subject | Deep neural networks | - |
dc.subject | Diseases | - |
dc.subject | Image enhancement | - |
dc.subject | Image recognition | - |
dc.subject | Learning algorithms | - |
dc.subject | Medical computing | - |
dc.subject | Medical imaging | - |
dc.subject | Medical problems | - |
dc.subject | Neural networks | - |
dc.subject | Oncology | - |
dc.subject | Piecewise linear techniques | - |
dc.subject | Activation functions | - |
dc.subject | Classification tasks | - |
dc.subject | Convolutional neural network | - |
dc.subject | Human malignancies | - |
dc.subject | Melanoma | - |
dc.subject | Melanoma detection | - |
dc.subject | Neural network model | - |
dc.subject | Skin cancers | - |
dc.subject | Dermatology | - |
dc.title | Convolutional Neural Network Algorithm with Parameterized Activation Function for Melanoma Classification | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.1109/ICTC.2018.8539451 | - |
dc.identifier.bibliographicCitation | 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018, pp.417 - 419 | - |
dc.identifier.scopusid | 2-s2.0-85059475854 | - |
dc.citation.endPage | 419 | - |
dc.citation.startPage | 417 | - |
dc.citation.title | 9th International Conference on Information and Communication Technology Convergence: ICT Convergence Powered by Smart Intelligence, ICTC 2018 | - |
dc.contributor.affiliatedAuthor | Namozov, A. | - |
dc.contributor.affiliatedAuthor | Cho, Y.I. | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | Convolutional Neural Networks | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Melanoma | - |
dc.subject.keywordAuthor | Skin Cancer | - |
dc.subject.keywordPlus | Chemical activation | - |
dc.subject.keywordPlus | Computer aided diagnosis | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Data mining | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.subject.keywordPlus | Diseases | - |
dc.subject.keywordPlus | Image enhancement | - |
dc.subject.keywordPlus | Image recognition | - |
dc.subject.keywordPlus | Learning algorithms | - |
dc.subject.keywordPlus | Medical computing | - |
dc.subject.keywordPlus | Medical imaging | - |
dc.subject.keywordPlus | Medical problems | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Oncology | - |
dc.subject.keywordPlus | Piecewise linear techniques | - |
dc.subject.keywordPlus | Activation functions | - |
dc.subject.keywordPlus | Classification tasks | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Human malignancies | - |
dc.subject.keywordPlus | Melanoma | - |
dc.subject.keywordPlus | Melanoma detection | - |
dc.subject.keywordPlus | Neural network model | - |
dc.subject.keywordPlus | Skin cancers | - |
dc.subject.keywordPlus | Dermatology | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.