1. THIRD CLASSIFICATION OF MICROCALCIFICATION STAGES IN MAMMOGRAPHIC IMAGES THIRD REVIEW Supervisor: Mrs.P.Valarmathi HOD/CSE Project Members: M.HamsaPriya(81210132028) N.Madhumathi(81210132044) M.Sneha(81210132086) S.Vijayarani(81210132108)

2. Objective : To classify the various stages of Benign and Malignant tumor in Mammography and improving the accuracy level.

3. Abstract: There are many classes of breast cancer with different characteristics. Techniques in image similarity can be used to improve the classification of breast cancer.

4. Introduction : Normal Breast:Affected Breast:

5. Diagnosis Methods: Mammograms Ultra- Sonography Aspiration Surgical Biopsy

6. Existing System: Breast cancer stages are classified into three types. Normal, Benign and Malignant. Computer-Aided Diagnosis(CAD) is the mainly used to detect tumor by comparing the images.

7. Limitations in Existing System: Poor performance is caused by high false- positive rates and the use of only one view. Less warranty in clinical usage.

8. Proposed System: Classification of Benign and Malignant tumor. Benign is further classified into Fibrocystic Masses, Cysts,Fibroadenomas,Intraductal Papillomas,Traumatic Fat Necrosis and Phylloides Tumors. Malignant is further classified into Carcinoma,Sarcoma,Leukemia,Lymphoma and Myeloma.

9. Advantages on Proposed System: Reduces false negatives by which duration for treatment and cost can be reduced. Reduces false positive.

10. System Architecture: Storing & Retrieving Images Histogram Preprocessing Feature Extraction and Selection Classifiers Combining Classifiers DATABASE

11. Conclusion: Thus the Benign and Malignant tumors are classified into various stages and its accuracy level is improved.

12. Future Enhancement: The best results obtained are around 95% which is not sufficient enough for implementation in clinical trials. Non-conventional techniques such as Neural Networks and SVM can be used to obtain more accuracy.

13. THANK YOU