A development of knowledge-based inferences system for detection of breast cancer on thermogram images

Thermography is considered as the most effective method for the detection of breast cancers. However, it is difficult for radiologists to predict Photomicrograph of Microcalcification clusters. Therefore, we have developed a computerized scheme for predicting early-stage microcalcification clusters in thermogram images. The gray and color channels of a thermogram image are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE).Optimal set of features selected by Genetic algorithm are fed as input to Adaptive Neuro-Fuzzy Inference System (ANFIS) for classification of images into normal, suspect and abnormal categories. The method has been evaluated on real time images comprising normal and abnormal images. The performance of the proposed technique is analyzed in terms convergence time. Experimental results shows that the features used are clinically significant for the accurate detection of breast cancer related tumor.