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Fusion of medical images using deep belief networks

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer and Communication Engineering, School of Computing and Information Technology, Manipal University Jaipur, Jaipur , Rajasthan, India

    Manjit Kaur

  2. Department of Computer Science and Engineering, School of Computing and Information Technology, Manipal University Jaipur, Jaipur , Rajasthan, India

    Dilbag Singh

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  1. Manjit Kaur
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Kaur, M., Singh, D. Fusion of medical images using deep belief networks. Cluster Comput 23, 1439–1453 (2020). https://doi.org/10.1007/s10586-019-02999-x

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