Semi-advised learning model for skin cancer diagnosis based on histopathalogical images

Masood, A; Al-Jumaily, A

Semi-advised learning model for skin cancer diagnosis based on histopathalogical images

Masood, A Al-Jumaily, A

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, 2016-October pp. 631 - 634
Issue Date:: 2016-10-13

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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.date.issued	2016-10-13	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, 2016-October pp. 631 - 634	en_US
dc.identifier.isbn	9781457702204	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122867
dc.description.abstract	© 2016 IEEE. Computer aided classification of skin cancer images is an active area of research and different classification methods has been proposed so far. However, the supervised classification models based on insufficient labeled training data can badly influence the diagnosis process. To deal with the problem of limited labeled data availability this paper presents a semi advised learning model for automated recognition of skin cancer using histopathalogical images. Deep belief architecture is constructed using unlabeled data by making efficient use of limited labeled data for fine tuning done the classification model. In parallel an advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data using advised weights. To increase generalization capability of the model, advised SVM and Deep belief network are trained in parallel. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 300 histopathalogical images taken from biopsy samples. The classification performance is compared with some popular methods and the proposed model outperformed most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization algorithm and transductive SVM based classification model.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2016.7590781	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Skin Neoplasms	en_US
dc.subject.mesh	Least-Squares Analysis	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Theoretical	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Image Processing, Computer-Assisted	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Semi-advised learning model for skin cancer diagnosis based on histopathalogical images	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-October	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
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	2016-October	en_US

Abstract:

© 2016 IEEE. Computer aided classification of skin cancer images is an active area of research and different classification methods has been proposed so far. However, the supervised classification models based on insufficient labeled training data can badly influence the diagnosis process. To deal with the problem of limited labeled data availability this paper presents a semi advised learning model for automated recognition of skin cancer using histopathalogical images. Deep belief architecture is constructed using unlabeled data by making efficient use of limited labeled data for fine tuning done the classification model. In parallel an advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data using advised weights. To increase generalization capability of the model, advised SVM and Deep belief network are trained in parallel. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 300 histopathalogical images taken from biopsy samples. The classification performance is compared with some popular methods and the proposed model outperformed most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization algorithm and transductive SVM based classification model.

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http://hdl.handle.net/10453/122867