Computer Aided Detection of solid breast nodules: Performance evaluation of Support Vector Machine and K- Nearest Neighbor classifiers

Breast Cancer is one of the major health concerns of women all over the world. Computer Aided Detection (CAD) aids radiologists for the early detection of abnormalities in the breast masses. Abnormalities in the breast may be cancerous or non cancerous. This work proposes an effective CAD system that considerably reduces the misclassification rates of these abnormalities. 60 mammogram images were taken and subjected to Segmentation and Feature Extraction techniques. K-means clustering algorithm is employed for segmentation and Fast Fourier Transform has been employed for the extraction of features. The unique set of feature vectors is given to the classification module. The classification of solid masses of breast nodule is done using Supervised Classifiers Support Vector Machine (SVM) and K- Nearest Neighbor (K- NN). The investigation reveals that S VM outperforms K- NN in terms of sensitivity, specificity and accuracy.