Adaptive activation functions for skin lesion classification using deep neural networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Namozov, A. | - |
dc.contributor.author | Ergashev, D. | - |
dc.contributor.author | Cho, Y.I. | - |
dc.date.available | 2020-02-27T12:43:48Z | - |
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/4380 | - |
dc.description.abstract | Skin cancer is considered one of the most common human malignancies, and melanoma is the deadliest form of this disease. Early detection can influence the outcome of the disease 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, skin lesion classification for melanoma 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 skin disease recognition. Experimental results show that a convolutional neural network model with adaptive piecewise linear units outperforms the same network with different activation functions in the skin lesion 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 | Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018 | - |
dc.subject | Chemical activation | - |
dc.subject | Computer aided diagnosis | - |
dc.subject | Convolution | - |
dc.subject | Deep learning | - |
dc.subject | Dermatology | - |
dc.subject | Diseases | - |
dc.subject | Image enhancement | - |
dc.subject | Image recognition | - |
dc.subject | Intelligent computing | - |
dc.subject | Intelligent systems | - |
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 | Soft computing | - |
dc.subject | Activation functions | - |
dc.subject | Adaptive activation function | - |
dc.subject | Convolutional neural network | - |
dc.subject | Human malignancies | - |
dc.subject | Melanoma detection | - |
dc.subject | Neural network model | - |
dc.subject | Piecewise linear | - |
dc.subject | Skin cancers | - |
dc.subject | Deep neural networks | - |
dc.title | Adaptive activation functions for skin lesion classification using deep neural networks | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000470750300037 | - |
dc.identifier.doi | 10.1109/SCIS-ISIS.2018.00048 | - |
dc.identifier.bibliographicCitation | Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018, pp.232 - 235 | - |
dc.identifier.scopusid | 2-s2.0-85067113394 | - |
dc.citation.endPage | 235 | - |
dc.citation.startPage | 232 | - |
dc.citation.title | Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018 | - |
dc.contributor.affiliatedAuthor | Namozov, A. | - |
dc.contributor.affiliatedAuthor | Ergashev, D. | - |
dc.contributor.affiliatedAuthor | Cho, Y.I. | - |
dc.type.docType | Proceedings Paper | - |
dc.subject.keywordAuthor | Activation Functions | - |
dc.subject.keywordAuthor | Convolutional Neural Networks | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Skin Cancer | - |
dc.subject.keywordPlus | Chemical activation | - |
dc.subject.keywordPlus | Computer aided diagnosis | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Dermatology | - |
dc.subject.keywordPlus | Diseases | - |
dc.subject.keywordPlus | Image enhancement | - |
dc.subject.keywordPlus | Image recognition | - |
dc.subject.keywordPlus | Intelligent computing | - |
dc.subject.keywordPlus | Intelligent systems | - |
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 | Soft computing | - |
dc.subject.keywordPlus | Activation functions | - |
dc.subject.keywordPlus | Adaptive activation function | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Human malignancies | - |
dc.subject.keywordPlus | Melanoma detection | - |
dc.subject.keywordPlus | Neural network model | - |
dc.subject.keywordPlus | Piecewise linear | - |
dc.subject.keywordPlus | Skin cancers | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.description.journalRegisteredClass | scopus | - |
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