A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues

• Published in: Journal of biomedical informatics Vol. 113; p. 103627
• Main Authors: Shamshirband, Shahab, Fathi, Mahdis, Dehzangi, Abdollah, Chronopoulos, Anthony Theodore, Alinejad-Rokny, Hamid
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2021
• Subjects: Deep neural network; Diagnostics tools; Health data analytics; Healthcare applications; Machine learning; FROC; Healthcare applications; DDSM; DNN; SNUH; DM; DoS; SPECT; AUC; LUAD; UM; FP-CIT; SWEDD; SFM; SDN; Health data analytics; STAD; PGBM; CAD; DAE; USF; Diagnostics tools; BRCA; Deep neural network; PD; Machine learning; BRATS; PPMI; SCDL; LIDC/IDRI
• ISSN: 1532-0464; 1532-0480; 1532-0480
• DOI: 10.1016/j.jbi.2020.103627

Taxonomy of Deep Learning architectures applied in the health care system. The mentioned applications in this figure are among those that have been widely investigated using DL models. [Display omitted] •Classification of the existing deep learning approaches in healthcare.•Providing extensive insights into the accuracy and applicability of deep learning models in healthcare solutions.•Discussing the core technologies which can reshape deep learning approaches in healthcare technologies.•Presenting open issues and challenges in current deep learning models in healthcare. In the last few years, the application of Machine Learning approaches like Deep Neural Network (DNN) models have become more attractive in the healthcare system given the rising complexity of the healthcare data. Machine Learning (ML) algorithms provide efficient and effective data analysis models to uncover hidden patterns and other meaningful information from the considerable amount of health data that conventional analytics are not able to discover in a reasonable time. In particular, Deep Learning (DL) techniques have been shown as promising methods in pattern recognition in the healthcare systems. Motivated by this consideration, the contribution of this paper is to investigate the deep learning approaches applied to healthcare systems by reviewing the cutting-edge network architectures, applications, and industrial trends. The goal is first to provide extensive insight into the application of deep learning models in healthcare solutions to bridge deep learning techniques and human healthcare interpretability. And then, to present the existing open challenges and future directions.