Self-supervised learning model for skin cancer diagnosis

Masood, A; Al-Jumaily, A; Anam, K

Self-supervised learning model for skin cancer diagnosis

Masood, A Al-Jumaily, A

Anam, K

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Publication Type:: Conference Proceeding
Citation:: International IEEE/EMBS Conference on Neural Engineering, NER, 2015, 2015-July pp. 1012 - 1015
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Masood, A	en_US
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463	en_US
dc.contributor.author	Anam, K https://orcid.org/0000-0003-4169-2237	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	International IEEE/EMBS Conference on Neural Engineering, NER, 2015, 2015-July pp. 1012 - 1015	en_US
dc.identifier.isbn	9781467363891	en_US
dc.identifier.issn	1948-3546	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35642
dc.description.abstract	© 2015 IEEE. Automated diagnosis of skin cancer is an active area of research with different classification methods proposed so far. However, classification models based on insufficient labeled training data can badly influence the diagnosis process if there is no self-advising and semi supervising capability in the model. This paper presents a semi supervised, self-advised learning model for automated recognition of melanoma using dermoscopic images. Deep belief architecture is constructed using labeled data together with unlabeled data, and fine tuning done by an exponential loss function in order to maximize separation of labeled data. In parallel a self-advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data. To increase generalization capability and redundancy of the model, polynomial and radial basis function based SA-SVMs and Deep network are trained using training samples randomly chosen via a bootstrap technique. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 100 dermoscopic images. The variation in classification error is analyzed with respect to the ratio of labeled and unlabeled data used in the training phase. The classification performance is compared with some popular classification methods and the proposed model using the deep neural processing outperforms most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization and transductive SVM.	en_US
dc.relation.ispartof	International IEEE/EMBS Conference on Neural Engineering, NER	en_US
dc.relation.isbasedon	10.1109/NER.2015.7146798	en_US
dc.title	Self-supervised learning model for skin cancer diagnosis	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015-July	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
dc.location.activity	Montpellier, France
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2015-July	en_US

Abstract:

© 2015 IEEE. Automated diagnosis of skin cancer is an active area of research with different classification methods proposed so far. However, classification models based on insufficient labeled training data can badly influence the diagnosis process if there is no self-advising and semi supervising capability in the model. This paper presents a semi supervised, self-advised learning model for automated recognition of melanoma using dermoscopic images. Deep belief architecture is constructed using labeled data together with unlabeled data, and fine tuning done by an exponential loss function in order to maximize separation of labeled data. In parallel a self-advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data. To increase generalization capability and redundancy of the model, polynomial and radial basis function based SA-SVMs and Deep network are trained using training samples randomly chosen via a bootstrap technique. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 100 dermoscopic images. The variation in classification error is analyzed with respect to the ratio of labeled and unlabeled data used in the training phase. The classification performance is compared with some popular classification methods and the proposed model using the deep neural processing outperforms most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization and transductive SVM.

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http://hdl.handle.net/10453/35642