RESEARCH ARTICLE
Multi-Channel Local Binary Pattern Guided Convolutional Neural Network for Breast Cancer Classification
Hiren Mewada1, *, Jawad F. Al-Asad1, Amit Patel2, Jitendra Chaudhari2, Keyur Mahant2, Alpesh Vala2
Article Information
Identifiers and Pagination:
Year: 2021Volume: 15
Issue: Suppl-2, M2
First Page: 132
Last Page: 140
Publisher ID: TOBEJ-15-132
DOI: 10.2174/1874120702115010132
Article History:
Received Date: 16/8/2020Revision Received Date: 11/1/2021
Acceptance Date: 13/1/2021
Electronic publication date: 31/12/2021
Collection year: 2021
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
The advancement in convolutional neural network (CNN) has reduced the burden of experts using the computer-aided diagnosis of human breast cancer. However, most CNN networks use spatial features only. The inherent texture structure present in histopathological images plays an important role in distinguishing malignant tissues. This paper proposes an alternate CNN network that integrates Local Binary Pattern (LBP) based texture information with CNN features.
Methods:
The study propagates that LBP provides the most robust rotation, and translation-invariant features in comparison with other texture feature extractors. Therefore, a formulation of LBP in context of convolution operation is presented and used in the proposed CNN network. A non-trainable fixed set binary convolutional filters representing LBP features are combined with trainable convolution filters to approximate the response of the convolution layer. A CNN architecture guided by LBP features is used to classify the histopathological images.
Result:
The network is trained using BreKHis datasets. The use of a fixed set of LBP filters reduces the burden of CNN by minimizing training parameters by a factor of 9. This makes it suitable for the environment with fewer resources. The proposed network obtained 96.46% of maximum accuracy with 98.51% AUC and 97% F1-score.
Conclusion:
LBP based texture information plays a vital role in cancer image classification. A multi-channel LBP futures fusion is used in the CNN network. The experiment results propagate that the new structure of LBP-guided CNN requires fewer training parameters preserving the capability of the CNN network’s classification accuracy.
