Abstract
Breast cancer is the most common type of cancer in women. It is a disease in which abnormal cells begin to grow and multiply uncontrollably. They form a tumor mass that can be either benign (non-cancerous) or malignant.
In the present work information for patients with breast cancer was analyzed. The data contains measurements (parameters), calculated from a digitized fine-needle aspirate image of a breast mass: type of formation, radius, perimeter, area, texture, smoothness, symmetry, concavity, etc.
For data analysis an InterCriteria Analysis method is used. The method uses indexed matrices and intuitionistic fuzzy estimations. By the method the correlations between each pairs of parameters, explaining the formation in the breast mass were obtained. The obtained correlations are in a form of intuitionistic fuzzy pairs with values in the [0, 1] interval.
The aim of the study is to propose a method for reducing the input data about breast cancer at the inputs of a Deep learning neural network. This can be easily done, but the goal here is to achieve a reduction in the number of neural network inputs without affecting the classification accuracy of the data. For this purpose, the obtained intuitionistic fuzzy pairs were used, showing the degree of connection between the measured parameters.
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Acknowledgment
This research was funded in part by the European Regional Development Fund through the Operational 268 Programme “Science and Education for Smart Growth” under contract UNITe. BG05M2OP001–1.001–0004 269 (2018–2023).”
The authors declare that there is no conflict of interest regarding the publication of this paper.
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Sotirova, E., Bozov, H., Stewart, R., Sotirov, S., Dobrev, P. (2023). On an Intelligent Hybrid Intuitionistic Fuzzy Method for Breast Cancer Classification. In: Kahraman, C., Sari, I.U., Oztaysi, B., Cebi, S., Cevik Onar, S., Tolga, A.Ç. (eds) Intelligent and Fuzzy Systems. INFUS 2023. Lecture Notes in Networks and Systems, vol 759. Springer, Cham. https://doi.org/10.1007/978-3-031-39777-6_9
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