Multi-objective breast cancer classification by using multi-expression programming

Despite many years of research, breast cancer detection is still a difficult, but very important problem to be solved. An automatic diagnosis system could establish whether a mammography presents tumours or belongs to a healthy patient and could offer, in this way, a second opinion to a radiologist...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2015-10, Vol.43 (3), p.499-511
Main Authors: Dioşan, Laura, Andreica, Anca
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Automation
Breast
Breast cancer
Cancer
Classification
Computer Science
Computers
Diagnosis
Evolutionary
Experiments
Genetic algorithms
Machines
Mammography
Manufacturing
Mechanical Engineering
Medical research
Processes
Tumors
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Summary:Despite many years of research, breast cancer detection is still a difficult, but very important problem to be solved. An automatic diagnosis system could establish whether a mammography presents tumours or belongs to a healthy patient and could offer, in this way, a second opinion to a radiologist that tries to establish a diagnosis. We therefore propose a system that could contribute to lowering both the costs and the work of an imaging diagnosis centre of breast cancer and in addition to increase the trust level in that diagnosis. We present a multi-objective evolutionary approach based on Multi-Expression Programming—a linear Genetic Programming method—that could classify a mammogram starting from a raw image of the breast. The processed images are represented through Histogram of Oriented Gradients and Kernel Descriptors since these image features have been reported as being very efficient in the image recognition scientific community and they have not been applied to mammograms before. Numerical experiments are performed on freely available datasets consisting of normal and abnormal film-based and digital mammograms and show the efficiency of the proposed decision support system.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-015-0668-8