Fuzzy convex set-based pattern classification for analysis of mammographic microcalcifications

Grohman, Wojciech; Dhawan, Atam P.

doi:10.1016/s0031-3203(00)00085-6

Cited by 11 publications

(3 citation statements)

References 24 publications

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“…A novel pattern recognition method called "NFPC" has been introduced for training a neurofuzzy classifier by the identification of convex subsets of patterns in the data space [48]. The performed tests ensured the accuracy of the proposed method with respect to other different classifiers.…”

Section: Related Workmentioning

confidence: 97%

Fuzzy Clustering Using the Convex Hull as Geometrical Model

Liparulo

Proietti

Panella

2015

Advances in Fuzzy Systems

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A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms.

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Section: Related Workmentioning

confidence: 97%

Fuzzy Clustering Using the Convex Hull as Geometrical Model

Liparulo

Proietti

Panella

2015

Advances in Fuzzy Systems

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“…We can use the same techniques in cancer diagnosis to assist doctors with their decisions. Since mammography is one of the preliminary tests performed to locate abnormalities in the breast tissue, it is used for screening purposes and has raised a lot of interest within the scientific community [47,48,49,50,51,52,53]. Since mammography is one of the preliminary tests performed to locate abnormalities in the breast tissue, it is used for screening purposes and has raised a lot of interest within the scientific community [47,48,49,50,51,52,53].…”

Section: Computer-aided Breast Cancer Diagnosismentioning

confidence: 99%

Computer Recognition Systems 4

2011

Advances in Intelligent and Soft Computing

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“…An intelligent clustering method for the detection of microclassification clusters in mammography was presented (24). Fuzzy sets and pattern classification are combined for classification of microcalcifications (25). Finally, computer aided diagnosis approaches in mammograms have also been proposed by Sittek et al (26) and Christoyianni et al (27).…”

Section: Introductionmentioning

confidence: 99%

Computational intelligence for the detection and classification of malignant lesions in screening mammography

Panourgias

Tsakonas²,

Dounias³

et al. 2006

Oncol Rep

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Abstract. This report deals with the discussion of the findings obtained from the application of two computational intelligence methodologies for the detection of microcalcifications in screening mammography data. Genetic programming and inductive machine learning have been applied, in order to produce meaningful diagnostic rules for the medical staff. The data used in the experiments correspond to information acquired from two images of each breast of the patient, along with some associated patient information such as the age at time of study. Similar datasets have been previously used in an attempt to facilitate the development of computer algorithms to aid screening. Experienced screening radiologists have double-read the screening mammograms, they have weighted the malignancy ratings and averaged out the levels of suspiciousness assigned to each finding in the screenings. The diagnostic rules which were obtained from both genetic programming and machine learning have been evaluated in detail and then analyzed and discussed by collaborative medical experts, in parallel to findings from related literature. Results seem encouraging for further use and analysis by medical staff specializing in screening mammography.

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Fuzzy convex set-based pattern classification for analysis of mammographic microcalcifications

Cited by 11 publications

References 24 publications

Fuzzy Clustering Using the Convex Hull as Geometrical Model

Fuzzy Clustering Using the Convex Hull as Geometrical Model

Computer Recognition Systems 4

Computational intelligence for the detection and classification of malignant lesions in screening mammography

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