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A swarm optimization-based Kmedoids clustering technique for extracting melanoma cancer features
conference contribution
posted on 2017-01-01, 00:00 authored by Seyedamin Khatami, S Mirghasemi, Abbas KhosraviAbbas Khosravi, Chee Peng LimChee Peng Lim, Houshyar AsadiHoushyar Asadi, Saeid Nahavandi© 2017, Springer International Publishing AG. Melanoma is a dangerous type of skin cancers. It is alarming to see the increase of this noxious disease in modern societies, however, it can be cured by surgical excision if it is detected early. In this paper, a swarm-based clustering technique for detecting melanoma is developed. Meaningful colour features from images are extracted, and a new objective function is introduced by applying an efficient and fast linear transformation to detect Melanoma. Specifically, the proposed technique consists of three main phases. The first phase is a pre-processing stage to organize data into proper attributes, while the subsequent two phases comprise iterative swarm optimisation procedures. The iterative swarm optimisation procedures involve a linear transformation to convert the existing colour components into a new colour space, formulation of the Kmedoids objective function, and error minimisation of the particle swarm optimisation (PSO) solutions. The Otsu threshold technique is utilised to provide binary images. The proposed technique is efficient and effective due to its linearity and simplicity.
History
Event
Neural Information Processing. International Conference (24th : 2017 : Guangzhou, China)Volume
10637Series
Lecture Notes in Computer SciencePagination
307 - 316Publisher
SpringerLocation
Guangzhou, ChinaPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2017-11-14End date
2017-11-18ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319700922Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2017, Springer International Publishing AGTitle of proceedings
ICONIP 2017: Proceedings of the 24th International Conference on Neural Information ProcessingUsage metrics
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