Extrinsically evolved system for breast cancer detection

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@Article{DBLP:journals/evi/KhalidKA24,

• author = "Zahra Khalid and Gul Muhammad Khan and Arbab Masood Ahmad",
• title = "Extrinsically evolved system for breast cancer detection",
• journal = "Evolutionary Intelligence",
• year = "2024",
• volume = "17",
• number = "2",
• pages = "731--743",
• month = apr,
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Breast cancer, Fine Needle Aspiration (FNA), Wisconsin Breast Cancer Database, Evolvable hardware",
• timestamp = "Mon, 01 Apr 2024 11:15:25 +0200",
• biburl = "https://dblp.org/rec/journals/evi/KhalidKA24.bib",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• DOI = "doi:10.1007/S12065-022-00752-9",
• 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