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Development of a fuzzy-driven system for ovarian tumor diagnosis

Żywica P., Dyczkowski K., Wójtowicz A., Stachowiak A., Szubert S., Moszyński R.

Biocybernetics and Biomedical Engineering

2016

Vol. 36, no. 4

632--643

In this paper we present OvaExpert, an intelligent system for ovarian tumor diagnosis. We give an overview of its features and main design assumptions. As a theoretical framework the system uses fuzzy set theory and other soft computing techniques. This makes it possible to handle uncertainty and incompleteness of the data, which is a unique feature of the developed system. The main advantage of OvaExpert is its modular architecture which allows seamless extension of system capabilities. Three diagnostic modules are described, along with examples. The first module is based on aggregation of existing prognostic models for ovarian tumor. The second presents the novel concept of an Interval-Valued Fuzzy Classifier which is able to operate under data incompleteness and uncertainty. The third approach draws from cardinality theory of fuzzy sets and IVFSs and leads to a bipolar result that supports or rejects certain diagnoses.

OvaExpert: an intelligent medical diagnosis support system for ovarian tumor

Żywica P., Apolinarski S., Kubiński B.

Journal of Medical Informatics & Technologies

2015

Vol. 24

183--190

In this paper we present OvaExpert, an intelligent system for ovarian tumor diagnosis. We give an overview of its features and main design assumptions. As a theoretical framework the system uses fuzzy set theory and other soft computing techniques. This makes it possible to handle uncertainty and incompleteness of the data which is an unique feature of developed system. The main advantage of OvaExpert is its modular architecture which allows seamless extension of system capabilities. Two diagnostic modules are described in the paper along with examples. First module is based on aggregation of existing prognostic models for ovarian tumor. Second, on novel concept of Interval– Valued Fuzzy Classifier which is able to operate under data incompleteness and uncertainty.