Feature Extraction with Automated Scale Selection in Skin Cancer Image Classification: A Genetic Programming Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{ul-ain:2024:GECCO,
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author = "Qurrat {Ul Ain} and Harith Al-Sahaf and Bing Xue and
Mengjie Zhang",
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title = "Feature Extraction with Automated Scale Selection in
Skin Cancer Image Classification: A Genetic Programming
Approach",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference",
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year = "2024",
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editor = "Ruhul Sarker and Patrick Siarry and Julia Handl and
Xiaodong Li and Markus Wagner and Mario Garza-Fabre and
Kate Smith-Miles and Richard Allmendinger and
Ying Bi and Grant Dick and Amir H Gandomi and
Marcella Scoczynski Ribeiro Martins and Hirad Assimi and
Nadarajen Veerapen and Yuan Sun and
Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and
Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
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pages = "1363--1372",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, feature
extraction, cancer detection, image classification,
Real World Applications",
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isbn13 = "979-8-4007-0494-9",
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DOI = "doi:10.1145/3638529.3654071",
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size = "10 pages",
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abstract = "Early detection of cancer is vital for reducing
mortality rates, but medical images come in various
resolutions, often captured from diverse devices, and
pose challenges due to high inter-class and intra-class
variability. Integrating various feature descriptors
enhances high-level feature extraction for improved
classification. Having varied structure sizes of tumor
characteristics in these medical images, extracting
features from a single scale might not provide
meaningful or discriminative features. Genetic
Programming (GP) proves effective in this context due
to its flexible representation and global search
capabilities. Unlike existing GP methods relying on
extracting features from a single scale of the input
image, this paper introduces a novel GP-based feature
learning approach that automatically selects scales and
combines image descriptors for skin cancer detection.
The method learns global features from diverse scales,
leading to improved classification performance on
dermoscopic and standard camera image datasets. The
evolved solutions not only enhance classification but
also pinpoint the most effective scales and feature
descriptors for different skin cancer image datasets.
The proposed method generates interpretable models,
aiding medical practitioners in diagnoses by
identifying cancer characteristics captured through
automatically selected feature descriptors in the
evolutionary process.",
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notes = "GECCO-2024 RWA A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Qurrat Ul Ain
Harith Al-Sahaf
Bing Xue
Mengjie Zhang
Citations