BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification
Article
Abdar, Moloud, Fahami, Mohammad Amin, Chakrabarti, Satarupa, Khosravi, Abbas, Pławiak, Paweł, Acharya, U. Rajendra, Tadeusiewicz, Ryszard and Nahavandi, Saeid. 2021. "BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification." Information Sciences. 577, pp. 353-378. https://doi.org/10.1016/j.ins.2021.07.024
Article Title | BARF: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification |
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ERA Journal ID | 17908 |
Article Category | Article |
Authors | Abdar, Moloud, Fahami, Mohammad Amin, Chakrabarti, Satarupa, Khosravi, Abbas, Pławiak, Paweł, Acharya, U. Rajendra, Tadeusiewicz, Ryszard and Nahavandi, Saeid |
Journal Title | Information Sciences |
Journal Citation | 577, pp. 353-378 |
Number of Pages | 26 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0020-0255 |
1872-6291 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.ins.2021.07.024 |
Web Address (URL) | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110201498&doi=10.1016%2fj.ins.2021.07.024&partnerID=40&md5=ddd053aecfc21f93a2cc785dfb44ad1c |
Abstract | Automatic medical image analysis (e.g., medical image classification) is widely used in the early diagnosis of various diseases. The computer-aided diagnosis (CAD) systems enable accurate disease detection and treatment. Nowadays, deep learning (DL)-based CAD systems have been able to achieve promising results in most of the healthcare applications. Also, uncertainty quantification in the existing DL methods have not gained enough attention in the field of medical research. To fill this gap, we propose a novel, simple and effective fusion model with uncertainty-aware module for medical image classification called Binary Residual Feature fusion (BARF). To deal with uncertainty, we applied the Monte Carlo (MC) dropout during inference to obtain the mean and standard deviation of the predictions. The proposed model has two main strategies: direct and cross validated using four different medical image datasets. Our experimental results demonstrate that the proposed model is efficient for medical image classification in real clinical settings. © 2021 Elsevier Inc. |
Keywords | Deep learning |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Export Date: 9 October 2023 |
Byline Affiliations | Deakin University |
Isfahan University of Technology, Iran | |
Kalinga Institute of Industrial Technology, India | |
Cracow University of Technology, Poland | |
Polish Academy of Sciences, Poland | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan | |
AGH University of Science and Technology, Poland |
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