Breast cancer detection on thermogram at preliminary stage by using fuzzy inferences system

S. Julian Savari Antony, S. Ravi

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Thermogram is considered as one of the most effective methods for early detection of breast cancers.However, it is difficult for radiologists to detect Microcalcification clusters. Therefore a computerized scheme for detecting early-stage Microcalcification clusters in mammograms is proposed. Optimal set of features are selected by Genetic algorithm which are fed as input to Adaptive Neuro fuzzy inference system for classifying image into normal, suspect and abnormal categories. This method has been evaluated on 322 images comprising normal and abnormal images. The performance of the proposed technique is analyzed in terms convergence time. The results shows that the features used are clinically significant for the accurate detection of breast tumor.

Original languageEnglish
Pages (from-to)381-391
Number of pages11
JournalJournal of Theoretical and Applied Information Technology
Volume68
Issue number3
Publication statusPublished - Jan 1 2014

Fingerprint

Fuzzy Inference System
Fuzzy inference
Breast Cancer
Microcalcifications
Tumors
Genetic algorithms
Adaptive Neuro-fuzzy Inference System
Mammogram
Convergence Time
Tumor
Genetic Algorithm
Term

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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Breast cancer detection on thermogram at preliminary stage by using fuzzy inferences system. / Julian Savari Antony, S.; Ravi, S.

In: Journal of Theoretical and Applied Information Technology, Vol. 68, No. 3, 01.01.2014, p. 381-391.

Research output: Contribution to journalArticle

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