Detection and Processing of Mammograms by using Neural Networks and Wavelets through OFDM
Pradeep Kumar

Dr. Pradeep Kumar, ECE, CMR Institute of Technology, Hyderabad, India.
Manuscript received on 24 August 2019. | Revised Manuscript received on 09 September 2019. | Manuscript published on 30 September 2019. | PP: 1996-1998 | Volume-8 Issue-11, September 2019. | Retrieval Number: K21600981119/2019©BEIESP | DOI: 10.35940/ijitee.K2160.0981119
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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 effective method for early detection of breast tumour. Recently due to machine learning development, it became easy to train with deep neural networks(DNN) by using convolutional neural networks(CNN) and computer aided diagnosis(CAD). The detected part is de-noised by wavelet transforms and it is transmitted through orthogonal frequency division multiplexing(OFDM) in case of treatment in remote area. Since most of the people still live in rural area with lack of awareness about breast cancer. Systems are trained on more number of data to obtain high sensitivity. The region of interest(ROI) is detected and segmented portion is processed for pixel-wise class prediction also with these most suitable techniques.
Keywords: Mammogram, ROI, Segmentation, Microcalcification, Neural Networks, Wavelet Transforms and OFDM.
Scope of the Article: