Breast Segmentation and Probable Region Identification for Breast Cancer using DL-CNN
Nagendra Kumar M1, Anand Jatti2, C K Narayanappa3
1Nagendra Kumar M*, Associate Professor Dept. of Electronics & Communication Engineering, S J C Institute of Technology, Chickballapur.
2Anand Jatti, Associate Professor, Dept. Of Electronics and Instrumentation Engineering, RV College of Engineering, Bengaluru.
3C K Narayanappa, Associate Professor, Dept. of Medical Electronics, M S Ramaiah Institute of Technology, Bengaluru.
Manuscript received on November 14, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 16-22 | Volume-9 Issue-2, December 2019. | Retrieval Number: A4172119119/2019©BEIESP | DOI: 10.35940/ijitee.A4172.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Mammography is one of the key method used for detecting the breast cancer, several researcher has proposed the detection and segmentation method, however absolute solution has not developed till now and they have certain limitation and still it is one of the major challenge for finding the region in masses. Hence in this research work we have developed and design a novel method named as DL-CNN (Dual Layered) architecture CNN. The main intention of the model is segmentation and probable region identification. DL-CNN is based on the Convolution Neural Network. It has two layer first layer is applied for identifying the probable region whereas the second layer is used for segmentation and minimizing the false positive Reduction. In order to evaluate the DL-CNN algorithm by using the In Breast Dataset. Moreover the proposed model is compared against the various model in terms of ROI(Region of Interest), Dice_ Index and False positive per Image. Result analysis shows that our model outperforms the existing
Keywords: CNN, DL-CNN, Segmentation, Probable Region Identification, Breast Cancer
Scope of the Article: Operational Research