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.
|Number of pages||11|
|Journal||Journal of Theoretical and Applied Information Technology|
|Publication status||Published - Jan 1 2014|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)