Breast Cancer Detection using Deep Learning Neural Network with Image Processing Techniques

Authors

  • R.Niranjana, A.Ravi, R.Vedhapriyavadhana, E.Francy Irudaya Rani, N.Narayanan Prasanth

Abstract

Breast cancer growth is a typical anomaly that influences a large sector of the ladies and the affected ladies would have less survival rate. Early recognition of the cancerous cells is a huge concern in decreasing the death rate. Nowadays deep learning approaches are used to characterize the disease. A programmed location framework is created to categorize the tumor as affable or dangerous from breast mammography pictures. Two sorts of division strategies are presented where the Region of interest (ROI) is resolved physically in the principal stage and in the subsequent stage, edge-based division is used. The Radial Basis Function Neural Network (RBFNN) has been employed for categorizing the cancerous cells into myriad varieties that are existing. Pre preparing of dataset images is achieved to expand the volume of information and to make the information of interest more prominent. Gabor filter is utilized for preprocessing and Gabor wavelet transform is utilized for segmentation. The Genetic algorithm is exploited for the extraction of features of interest.  Through this way, the recognition precision is improved with the recently prepared RBFNN design. The performance of the proposed approach is compared with existing approaches to justify its accuracy and ability through MATLAB simulations.

Keywords-radial basis function; neural network; gabor filter; gabor wavelet transform; genetic algorithm

Published

2020-11-26

Issue

Section

Articles