• Andrea Paulin
• Aiswarya

Breast cancer is a disease in which the cells of the breast grow out of control, creates an abnormality in the breast tissue. It is the second leading cause of death in women worldwide. In Saudi Arabia, Ministry of health reported that the number of new cases of cancer is 2741 including about 19.9% of breast cancer in women due to unawareness , it usually occurs in women at the age of 52. It accounts for about 22% of all new cancers in women. In developing countries there are still large numbers of breast cancers diagnosed in later stages. So the death rate is also high. To prevent people from this disease, it should be detected at an earlier stage which reduces death rate. Digital mammogram is used for this purpose. The suspected symptoms causing breast cancer are age, post menopause, stress, family history, physical inactivity, obesity, hormonal imbalances and genetically mutated abnormalities. Our work focus on classification of normal, benign and malignant into five stage of breast cancer using image processing techniques and data mining technique is used to classify the stage of breast cancer and the performance of classifier is evaluated through confusion matrix. Image is preprocessed by studying various parameters extractions such as color conversion, resizing and filtering. K means clustering segmentation is carried out by segmentation algorithms . This helps to identify the amount of lesions scattered over the body. Feature extraction is by threshold and finally, Approximate reasoning method to recognize the tumor shape and position in MRI image using DCNN algorithm classification method. Finally the message box will be displayed whether it is benign or malignant.

PNG Portable Network Graphics JPEG Joint Photographic Experts Group CAD Computer Aided Diagnosis SWE Shear wear Elastography ROC Receiver Operating Characteristics CNR Contrast to Noise Ratio SNR Signal to Noise Ratio SVM Support Vector Machines CNN Convolution Neural Networks GLCM Gray Level Co- ocurrence Matrix LDA Linear Discriminant Analysis MIAS Mammography Image Analysis Society PGM Portable Gray Map MAT Lab Matrix Laboratory API Application Program Interface MRI Magnetic Resonance Imaging X - Ray X- Radiation SVM Support Vector Machine PIOS Program Information Online System DCNN Deep Convolution Neural Network

"Mammogram image classification using deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.b48-b53, June-2022, Available :http://www.jetir.org/papers/JETIR2206110.pdf

"Mammogram image classification using deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppb48-b53, June-2022, Available at : http://www.jetir.org/papers/JETIR2206110.pdf