Deep learning for Early Diagnosis of Breast cancer using mammogram images

D.D. Pukale, Prajakta Dagade, Sampada Dagwar, Sayali Mohite, Rohini Zade


Breast cancer is one of the frequent diagnosis diseases among women. The X-ray mammography is the main test used for screening of detection of breast cancer with early diagnosis, and its analysis and processing are the keys to improving breast cancer diagnosis. The problem with mammography images are complex and usually of low contrast and noisy. Therefore, detection of cancerous lesions in mammogram images becomes an active research area. Mammogram image is input to the system then different pre-processing phases are performed.

Then Segmentation of image is done. In this we have applied two different algorithm, sliding window algorithm and dispersed region growing algorithm for removing pectoral muscle and finding the region of interest respectively. Segmented image given to Deep Convolution neural network(DCNN). Based on the feature extraction, classification of tumor is done stages wise and corresponding treatment is suggested.

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