Fusion of medical images using deep belief networks

  • Manjit Kaur
  • Dilbag SinghEmail author


Image fusion plays a significant role in various computer vision applications. However, designing an efficient image fusion technique is still a challenging task. In this paper, a novel deep belief networks based image fusion framework is proposed to improve the performance of the image fusion process further. We have initially, evaluated the fusion dataset by applying various feature extraction techniques. Thereafter, features selection techniques are applied to select potential features. Finally, the image fusion machine learning model is built by using a deep belief network model. Extensive experiments reveal that the proposed technique outperforms existing image fusion techniques.


Image fusion Deep belief networks Medical images Fuzzy rules 


Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer and Communication EngineeringSchool of Computing and Information Technology, Manipal University JaipurJaipur India
  2. 2.Department of Computer Science and EngineeringSchool of Computing and Information Technology, Manipal University JaipurJaipur India

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