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Automated diagnosis of breast cancer using multi-modal datasets: A deep convolution neural network based approach - 2022

Automated Diagnosis Of Breast Cancer Using Multi-Modal Datasets: A Deep Convolution Neural Network Based Approach

Research Paper on Automated Diagnosis Of Breast Cancer Using Multi-Modal Datasets: A Deep Convolution Neural Network Based Approach

Research Area:  Machine Learning

Abstract:

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