Multi-Model Analysis of Mammograms - Kochari, Vijaylaxmi K (International Journal of Computer Sciences and Engineering, 2021)
In this paper mammogram classification is introduced. The system takes mammogram, pre-processes it by applying Adaptive Histogram Equalization. The enhanced image is segmented using K Means Clustering algorithm. Statistical features such as standard deviation and mean of a segmented mammogram are extracted. SVM takes these features as input. DCT is applied on the segmented mammogram, these extracted features are fed as input to FFBPNN. These classify the mammogram as normal or abnormal. The training time of both the classifiers are compared to know which classifier takes less training time. The accuracy of the classifiers are determined by analyzing the results.