Exploring Genetic Programming Models in Computer-Aided Diagnosis of Skin Cancer Images
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{ain:2024:CEC,
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author = "Qurrat UI Ain and Harith Al-Sahaf and Bing Xue and
Mengjie Zhang",
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title = "Exploring Genetic Programming Models in
{Computer-Aided} Diagnosis of Skin Cancer Images",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming,
Representation learning, Visualization, Computational
modeling, Feature extraction, Skin, Lesions, Image
Classification, Skin Cancer Detection",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612105",
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abstract = "Extracting important information from complex skin
lesion images is vital to effectively distinguish
between different types of skin cancer images. In
addition to providing high classification performance,
such computer-aided diagnostic methods are needed where
the models are interpretable and can provide knowledge
about the discriminative features in skin lesion
images. This underlying information can significantly
assist dermatologists in identifying a particular stage
or type of cancer. With its flexible representation and
global search abilities, Genetic Programming (GP) is an
ideal learning al-gorithm to evolve interpretable
models and identify important features with significant
information to discriminate between skin cancer
classes. This paper provides an in-depth analysis of a
recent GP-based feature learning method where different
well-developed feature descriptors are integrated into
the learning algorithms to extract high-level features
for skin cancer image classification. The study
explores the effectiveness of using feature learning
for this complex task and designing program structure
to suit the problem domain as it has shown promising
results compared to commonly used feature descriptors
and an existing GP-based feature learning method
developed for general image classification. This study
analyses the GP-evolved models to identify the
prominent features and most effective feature
descriptors important for the classification of these
skin cancer images. The evolved models are
interpretable, they provide knowledge that can assist
dermatologists in making diagnoses in real-time
clinical situations by identifying prominent skin
cancer characteristics captured by the feature
descriptors and learnt during the evolutionary
process.",
-
notes = "also known as \cite{10612105}
WCCI 2024",
- }
Genetic Programming entries for
Qurrat Ul Ain
Harith Al-Sahaf
Bing Xue
Mengjie Zhang
Citations