Research Area:  Machine Learning
This paper proposes a deep convolutional neural network (CNN) model for automated breast cancer classification from a different class of images, namely, mammograms and ultrasound. The model contains only five learnable layers: four convolutional layers and a fully connected layer. The model facilitates extracting prominent features automatically from the images with a smaller number of tunable parameters. Exhaustive simulation results on mammograms dataset, namely, MIAS, DDSM, and INbreast, as well as ultrasound datasets, namely, BUS-1 and BUS-2, depict that the suggested model outperforms the recent state-of-the-art schemes. Data augmentation technique has been employed to reduce overfitting and provide good generalization. The proposed CNN model achieves an accuracy of 96.55%, 90.68%, and 91.28% on MIAS, DDSM, and INbreast datasets, respectively. Similarly, the accuracies obtained are 100% and 89.73% on BUS-1 and BUS-2 datasets, respectively.
Keywords:  
Automated Diagnosis
Breast Cancer
Multi-Modal Datasets
Deep Convolution Neural Network
Deep Learning
Machine Learning
Author(s) Name:  Debendra Muduli, Ratnakar Dash, Banshidhar Majhi
Journal name:  Biomedical Signal Processing and Control
Conferrence name:  
Publisher name:  Elsevier
DOI:  10.1016/j.bspc.2021.102825
Volume Information:  Volume 71, Part B, January 2022, 102825
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S1746809421004225