TY  - JOUR
AU  - Ibrišimović, Irma
AU  - Iričanin, Bratislav
AU  - Milosavljević, Nataša
AU  - Nedović, Ljubo
AU  - Ralević, Nebojša
PY  - 2023
UR  - https://link.springer.com/chapter/10.1007/978-3-031-39777-6_13
UR  - http://aspace.agrif.bg.ac.rs/handle/123456789/6463
AB  - Fuzzy sets are a mathematical notion that allows us to represent uncertainty by assigning a degree of membership to a value in a range. This degree of membership represents the degree to which a particular value belongs to the fuzzy set. Fuzzy numbers are fuzzy sets that satisfy specific conditions, and can be applied in the interpretation of the behavior of neural networks. In many real-world problems, the output of a neural network may not be a precise numerical value, but rather a range of values that are subject to uncertainty. In the context of neural networks, fuzzy numbers can be used to represent uncertainty in the output of the network. Fuzzy numbers are used in applications where the representation of ambiguity and uncertainty in numerical data is explicitly desired. This paper will consider discretizations and defuzzifications triangular fuzzy numbers with the use of MATLABR software. MATLABR functions were applied to the analysis of kidney images, where the fuzzy numbers represent the tendency value of the degree of redness, which serves as an indicator for classifying the image as normal or abnormal based on different characteristics.
T1  - Fuzzy Numbers and Analysis of Radiological Images
DO  - 10.1007/978-3-031-39777-6_13
ER  - 

@article{
author = "Ibrišimović, Irma and Iričanin, Bratislav and Milosavljević, Nataša and Nedović, Ljubo and Ralević, Nebojša",
year = "2023",
abstract = "Fuzzy sets are a mathematical notion that allows us to represent uncertainty by assigning a degree of membership to a value in a range. This degree of membership represents the degree to which a particular value belongs to the fuzzy set. Fuzzy numbers are fuzzy sets that satisfy specific conditions, and can be applied in the interpretation of the behavior of neural networks. In many real-world problems, the output of a neural network may not be a precise numerical value, but rather a range of values that are subject to uncertainty. In the context of neural networks, fuzzy numbers can be used to represent uncertainty in the output of the network. Fuzzy numbers are used in applications where the representation of ambiguity and uncertainty in numerical data is explicitly desired. This paper will consider discretizations and defuzzifications triangular fuzzy numbers with the use of MATLABR software. MATLABR functions were applied to the analysis of kidney images, where the fuzzy numbers represent the tendency value of the degree of redness, which serves as an indicator for classifying the image as normal or abnormal based on different characteristics.",
title = "Fuzzy Numbers and Analysis of Radiological Images",
doi = "10.1007/978-3-031-39777-6_13"
}

Ibrišimović, I., Iričanin, B., Milosavljević, N., Nedović, L.,& Ralević, N.. (2023). Fuzzy Numbers and Analysis of Radiological Images. .
https://doi.org/10.1007/978-3-031-39777-6_13

Ibrišimović I, Iričanin B, Milosavljević N, Nedović L, Ralević N. Fuzzy Numbers and Analysis of Radiological Images. 2023;.
doi:10.1007/978-3-031-39777-6_13 .

Ibrišimović, Irma, Iričanin, Bratislav, Milosavljević, Nataša, Nedović, Ljubo, Ralević, Nebojša, "Fuzzy Numbers and Analysis of Radiological Images" (2023),
https://doi.org/10.1007/978-3-031-39777-6_13 . .