- author = "Qurrat Ul Ain",
- title = "Genetic Programming based Feature Manipulation for Skin Cancer Image Classification",
- school = "Victoria University of Wellington",
- year = "2020",
- address = "New Zealand",
- keywords = "genetic algorithms, genetic programming, Image classification, Melanoma detection, Feature selection, Feature Construction, Skin cancer detection, ANN",
- broken = "http://hdl.handle.net/10063/9392",
-
URL = "
http://researcharchive.vuw.ac.nz/handle/10063/9392",
-
URL = "
http://researcharchive.vuw.ac.nz/bitstream/handle/10063/9392/thesis_access.pdf",
- size = "258 pages",
-
abstract = "Skin image classification involves the development of
computational methods for solving problems such as
cancer detection in lesion images,and their use for
biomedical research and clinical care. Such methods aim
at extracting relevant information or knowledge from
skin images that can significantly assist in the early
detection of disease. Skin images are enormous, and
come with various artifacts that hinder effective
feature extraction leading to inaccurate
classification. Feature selection and feature
construction can significantly reduce the amount of
data while improving classification performance by
selecting prominent features and constructing
high-level features. Existing approaches mostly rely on
expert intervention and follow multiple stages for
pre-processing, feature extraction, and classification,
which decreases the reliability, and increases the
computational complexity. Since good generalization
accuracy is not always the primary objective,
clinicians are also interested in analysing specific
features such as pigment network, streaks, and blobs
responsible for developing the disease; interpretable
methods are favoured. In Evolutionary Computation,
Genetic Programming (GP) can automatically evolve an
interpretable model and address the curse of
dimensionality (through feature selection and
construction). GP has been successfully applied to many
areas, but its potential for feature selection, feature
construction, and classification in skin images has not
been thoroughly investigated.
The overall goal of this thesis is to develop a new GP approach to skin image classification by using GP to evolve programs that are capable of automatically selecting prominent image features, constructing new high-level features, interpreting useful image features which can help dermatologist to diagnose a type of cancer, and are robust to processing skin images captured from specialized instruments and standard cameras. This thesis focuses on using a wide range of texture, color, frequency-based, local,and global image properties at the terminal nodes of GP to classify skin cancer images from multiple modalities effectively.
This thesis develops new two-stage GP methods using embedded and wrapper feature selection and construction approaches to automatically generating a feature vector of selected and constructed features for classification. The results show that wrapper approach outperforms the embedded approach, the existing baseline GP and other machine learning methods, but the embedded approach is faster than the wrapper approach.
This thesis develops a multitree GP based embedded feature selection approach for melanoma detection using domain specific and domain independent features. It explores suitable crossover and mutation operators to evolve GP classifiers effectively and further extends this approach using a weighted fitness function. The results show that these multi-tree approaches outperformed single tree GP and other classification methods. They identify that a specific feature extraction method extracts most suitable features for particular images taken from a specific optical instrument.
This thesis develops the first GP method using frequency-based wavelet features, where the wrapper based feature selection and construction methods automatically evolve useful constructed features to improve the classification performance. The results show the evidence of successful feature construction by significantly outperforming existing GP approaches, state-of-the-art CNN, and other classification methods.
This thesis develops a GP approach to multiple feature construction for ensemble learning in classification. The results show that the ensemble method outperformed existing GP approaches, state-of-the-art skin image classification, and commonly used ensemble methods. Further analysis of the evolved constructed features identified important image features that can potentially help the dermatologist identify further medical procedures in real-world situations.",
- notes = "supervisors Bing Xue and Mengjie Zhang and Harith Al-Sahaf",
