Extrinsically evolved system for breast cancer detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{DBLP:journals/evi/KhalidKA24,
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author = "Zahra Khalid and Gul Muhammad Khan and
Arbab Masood Ahmad",
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title = "Extrinsically evolved system for breast cancer
detection",
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journal = "Evolutionary Intelligence",
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year = "2024",
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volume = "17",
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number = "2",
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pages = "731--743",
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month = apr,
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, Breast cancer, Fine Needle
Aspiration (FNA), Wisconsin Breast Cancer Database,
Evolvable hardware",
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timestamp = "Mon, 01 Apr 2024 11:15:25 +0200",
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biburl = "https://dblp.org/rec/journals/evi/KhalidKA24.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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DOI = "doi:10.1007/S12065-022-00752-9",
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abstract = "Standard method of assessing breast cancer is a triple
test assessment. In this method, initially a thorough
medical examination and patient history is evaluated,
secondly imaging of the breast using x-rays and/or
ultrasound is done and finally a preoperative
cytodiagnosis is done that is either Fine Needle
Aspiration Cytology (FNAC) or Core Needle Biopsy (CNB)
or both. FNAC being a minimally invasive and rapidly
performed test is preferred in many cases over CNB that
is more invasive. If a triple test gives positive
result in any one of the three steps then the result is
taken positive. FNAC involves determining the cell size
and shape parameters and based on their values a case
is classified as benign or malignant. To automate the
process of decision making a novel technique has been
proposed. In this technique a digital logic circuit was
evolved using Cartesian Genetic Programming (CGP). A
CGP network was trained and then tested with FNAC
feature data from the Breast Cancer Wisconsin Dataset.
The dataset consists of 669 samples, of which 350
samples were used for training purposes and then the
trained system was evaluated with 349 test samples. A
number of experiments were performed, each with a
different set of network parameters. The best evolved
network classified the samples with an accuracy of
99.42 percent, which is higher than that produced with
most of the contemporary techniques. The network so
produced can be implemented on re-configurable
hardware.",
- }
Genetic Programming entries for
Zahra Khalid
Gul Muhammad Khan
Arbab Masood Ahmad
Citations