1874-1207/21 Send Orders for Reprints to reprints@benthamscience.net 132 DOI: 10.2174/1874120702115010132, 2021, 15, (Suppl-2, M2) 132-140 The Open Biomedical Engineering Journal Content list available at: https://openbiomedicalengineeringjournal.com RESEARCH ARTICLE Multi-Channel Local Binary Pattern Guided Convolutional Neural Network for Breast Cancer Classification Hiren Mewada 1,* , Jawad F. Al-Asad 1 , Amit Patel 2 , Jitendra Chaudhari 2 , Keyur Mahant 2 and Alpesh Vala 2 1 Electrical Engineering Department, Prince Mohammad Bin Fahd University, Al Khobar, Kingdom of Saudi Arabia 2 CHARUSAT Space Research and Technology Center, Charotar University of Science and Technology, Changa, Gujarat, India 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. Keywords: Convolutional neural network, Local binary pattern, Breast cancer, Histopathological images, Medical imaging, Accuracy. Article History Received: August 16, 2020 Revised: January 11, 2021 Accepted: January 13, 2021 1. INTRODUCTION Breast cancer is the most prevalent form of cancer in women, comprising 14 percent of Indian women's cancers. In both rural and urban India, breast cancer seems to be on the rise. A Breast Cancer Statistics 2018 survey [1] revealed a total of 1,62,468 new registered cases, and 87,090 reported deaths in a year. In higher stages of development, the survival of cancer is challenging, with over 50 percent of Indian women living * Address correspondence to this author at Department of Electrical Engineering, Prince Mohammad Bin Fahd University, P.O. Box: 1664, Al Khobar, 31952, Kingdom of Saudi Arabia; Tel/Fax: +966-13-849-9786; E-mail: hmewada@pmu.edu.sa with stage 3 and 4 breast cancer. Post-cancer survival was registered at 60% for breast cancer women in comparison with the U.S. having 80%. The biopsy is a diagnostic procedure commonly used to collect tissue samples of a human subject and analyze the existence or extent of a disease by a pathologist using a microscope. For observation, these tissues are processed and stained. Optical coherence tomography (OCT) provides an innovative non-invasive modality of optical imaging that can provide three-dimensional, high-resolution images of biological tissue structures omitting the requirement of the staining process [2]. The classification of breast tissue types with optical coherence microscopy (OCM) can be