MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

Senousy, Zakaria and Abdelsamea, Mohammed M. and Gaber, Mohamed Medhat and Abdar, Moloud and Acharya, Rajendra U. and Khosravi, Abbas and Nahavandi, Saeid (2021) MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification. IEEE Transactions on Biomedical Engineering. ISSN 0018-9294

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Abstract

Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multilevel Context and Uncertainty aware (MCUa) dynamic deep learning ensemble model. MCUa model consists of several multi-level context aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUa model has achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models.

Item Type:	Article
Identification Number:	10.1109/TBME.2021.3107446
Dates:	Date Event 21 August 2021 Accepted 30 August 2021 Published Online
Uncontrolled Keywords:	breast cancer, histology images, convo-lutional neural networks, context-awareness, uncertainty quantification
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Mohammed Abdelsamea
Date Deposited:	27 Oct 2021 13:11
Last Modified:	06 Jun 2022 14:14
URI:	https://www.open-access.bcu.ac.uk/id/eprint/12325

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