Feature Extraction with Automated Scale Selection in Skin Cancer Image Classification: A Genetic Programming Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.7941

@InProceedings{ul-ain:2024:GECCO,

• author = "Qurrat {Ul Ain} and Harith Al-Sahaf and Bing Xue and Mengjie Zhang",
• title = "Feature Extraction with Automated Scale Selection in Skin Cancer Image Classification: A Genetic Programming Approach",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference",
• year = "2024",
• 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",
• pages = "1363--1372",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, feature extraction, cancer detection, image classification, Real World Applications",
• isbn13 = "979-8-4007-0494-9",
• DOI = "doi:10.1145/3638529.3654071",
• size = "10 pages",
• 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.",
• 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