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A Survey on Hybrid Case-Based Reasoning and Deep Learning Systems for Medical Data Classification

A Survey on Hybrid Case-Based Reasoning and Deep Learning Systems for Medical Data Classification

Gasmi Safa, Djebbar Akila, Merouani Hayet Farida
Copyright: © 2022 |Pages: 29
ISBN13: 9781799890164|ISBN10: 1799890163|ISBN13 Softcover: 9781799890171|EISBN13: 9781799890188
DOI: 10.4018/978-1-7998-9016-4.ch006
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MLA

Safa, Gasmi, et al. "A Survey on Hybrid Case-Based Reasoning and Deep Learning Systems for Medical Data Classification." Handbook of Research on Foundations and Applications of Intelligent Business Analytics, edited by Zhaohao Sun and Zhiyou Wu, IGI Global, 2022, pp. 113-141. https://doi.org/10.4018/978-1-7998-9016-4.ch006

APA

Safa, G., Akila, D., & Farida, M. H. (2022). A Survey on Hybrid Case-Based Reasoning and Deep Learning Systems for Medical Data Classification. In Z. Sun & Z. Wu (Eds.), Handbook of Research on Foundations and Applications of Intelligent Business Analytics (pp. 113-141). IGI Global. https://doi.org/10.4018/978-1-7998-9016-4.ch006

Chicago

Safa, Gasmi, Djebbar Akila, and Merouani Hayet Farida. "A Survey on Hybrid Case-Based Reasoning and Deep Learning Systems for Medical Data Classification." In Handbook of Research on Foundations and Applications of Intelligent Business Analytics, edited by Zhaohao Sun and Zhiyou Wu, 113-141. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-9016-4.ch006

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Abstract

Several artificial intelligence approaches, particularly case-based reasoning (CBR), which is analogous to the context of human reasoning for problem resolution, have demonstrated their efficiency and reliability in the medical field. In recent years, deep learning represents the latest iteration of an advance in artificial intelligence technologies in medicine to aid in data classification, diagnosis of new diseases, and complex decision-making. Although these two independent approaches have good results in the medical field, the latter is still a complex field. This chapter reviews the available literature on CBR systems, deep learning systems, and CBR deep learning systems in medicine. The methods used and results obtained are discussed, and key findings are highlighted. Further, in the light of this review, some directions for future research are given. This chapter presents the proposed approach, which helps to make the retrieval phase of the CBR cycle more reliable and robust.

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