A hybrid classifier for mass classification with different kinds of features in mammography

Ping Zhang, Kuldeep Kumar, Brijesh Verma

Research output: Contribution to journalArticleResearchpeer-review

7 Citations (Scopus)

Abstract

This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.

Original languageEnglish
Pages (from-to)316-319
Number of pages4
JournalLecture Notes in Computer Science
Volume3614
Issue numberPART II
DOIs
Publication statusPublished - 2005

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Mammography
Classifiers
Classifier
Hybrid systems
Hybrid Systems
Neural Networks
Neural networks
Genetic Network
Mammogram
Receiver Operating Characteristic Curve
Output

Cite this

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abstract = "This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3{\%}. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.",
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A hybrid classifier for mass classification with different kinds of features in mammography. / Zhang, Ping; Kumar, Kuldeep; Verma, Brijesh.

In: Lecture Notes in Computer Science, Vol. 3614, No. PART II, 2005, p. 316-319.

Research output: Contribution to journalArticleResearchpeer-review

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AB - This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.

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