Krill herd optimization algorithm with deep convolutional neural network fostered breast cancer classification using mammogram images

Abstract

SummaryIn this paper proposes a Krill Herd Optimization algorithm with Deep Convolutional neural network fostered Breast Cancer Classification using Mammogram Images (BC‐APPDRC‐DCNN‐KHO). Here, the input images are taken from Real time and MAMMOSET datasets. These images are pre‐processed using Altered Phase Preserving Dynamic Range Compression (APPDRC) technique. This APPDRC is applied for preserving local features, compressing dynamic range of images, and enhancing the speckle noise filtering, these are all necessary for better boundary detection. Then, the Pre‐processed images are classified using Deep Convolutional neural network (DCNN). The DCNN weight parameters are optimized based on Krill Herd Optimization algorithm. The Proposed BCC‐DCNN‐KHO‐MI method classifies the input breast cancer imageries into 3 categories: benign, malignant, and normal. The proposed BCC‐DCNN‐KHO‐MI method in Real time dataset attains 18.505%, 19.45%, 16.19%, 17.56% and 16.19% higher accuracy; 15.38%, 12.06%, 12.71%, 26.62% and 18.902% higher Precision; 3.12%, 10.52%, 13.57%, 22.75% and 14.93% higher F‐score, 59.56%, 41.25%, 56.47%, 42.36% and 37.27% lower computation time; 23.87%, 21.87%, 32.87%, 42.76% and 21.05% higher AUC compared with the existing methods, like BCC‐Google Net‐MI, BCC‐Visual Geometry Group Network‐MI, BCC‐Residual Networks‐MI, BC‐RERNN‐LOA‐MI and BC‐CNN‐MI respectively.