RT Conference Proceedings
T1 Hybrid Modified Firefly Algorithm for Border Detection of Skin Lesions in Medical Imaging
A1 Galvez, Akemi
A1 Fister, Iztok
A1 Osaba, Eneko
A1 Fistar, Iztok
A1 Del Ser, Javier
A1 Iglesias, Andres
AB Computerized analysis of skin lesions is an important issue in information retrieval for medical imaging, as it helps human specialists to improve their decision-making for prompt and accurate diagnosis of melanoma and other skin diseases. A relevant task in this regard is border detection, which gives valuable information about some clinical features of skin lesions. This task is typically carried out manually by the dermatologists, leading to errors inherent to subjective diagnosis. In this paper, we address this problem by applying a modification of a powerful evolutionary computation method, the firefly algorithm. The modified algorithm is hybridized with a local search procedure for better performance. Experimental results on a benchmark of medical images of skin lesions show that this method outperforms classical mathematical methods for the instances in the benchmark and is very competitive and often superior to state-of-the-art techniques in the field in terms of numerical accuracy. We conclude that the approach is very promising and can be useful in real-world medical applications where speed is not a critical factor.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9781728121536
YR 2019
FD 2019-06
LK https://hdl.handle.net/11556/1879
UL https://hdl.handle.net/11556/1879
LA eng
NO Galvez , A , Fister , I , Osaba , E , Fistar , I , Del Ser , J & Iglesias , A 2019 , Hybrid Modified Firefly Algorithm for Border Detection of Skin Lesions in Medical Imaging . in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings . , 8789954 , 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings , Institute of Electrical and Electronics Engineers Inc. , pp. 111-118 , 2019 IEEE Congress on Evolutionary Computation, CEC 2019 , Wellington , New Zealand , 10/06/19 . https://doi.org/10.1109/CEC.2019.8789954
NO conference
NO Publisher Copyright: © 2019 IEEE.
NO Akemi Gálvez and Andrés Iglesias acknowledge the financial support from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 778035, and from the the Spanish Ministry of Science, Innovation and Universities (Computer Science National Program) under grant #TIN2017-89275-R of the Agencia Estatal de Investigación and European Funds FEDER (AEI/FEDER, UE). Iztok Fister and Iztok Fister Jr. acknowledge the financial support from the Slovenian Research Agency (Research Core Founding No. P2-0041 and P2-0057). Eneko Osaba and Javier Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program.
DS TECNALIA Publications
RD 28 jul 2024