Micro-Calcification Classification Analysis in Mammogram Images with Aid of Hybrid Technique Analysis

Breast cancer is the leading cause of death in women. Early identification can contribute significantly to improving survival rate. For diagnosis and accurate therapy automatic detection micro-calcification therefore essential. In paper, an automated technique utilized mammogram images according their classification. The working with combination Deep Belief Neural Network (DBNN) Chimp Optimization Algorithm (COA). proposed method three phases such as pre-processing phase, feature extraction, classification phase. a median filter remove unwanted information from images. extraction Gray Level Co-Occurrence Matrix (GLCM), Scale-Invariant Feature Transform (SIFT), Hu moments are extract essential features After that, performed on micro-calcifications utilization advanced deep learning method. From stage, normal abnormal identified implemented MATLAB platform analyzed statistical performances like accuracy, sensitivity, specificity, precision, recall, F-measure. To evaluate effectiveness this compared existing Support Vector Machine (SVM), Random Forest (RF), Artificial (ANN).