• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology

Article

Medical Image Fusion Using CNN with Automated Pooling
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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i42.1812

Year: 2022, Volume: 15, Issue: 42, Pages: 2267-2274

Original Article

Medical Image Fusion Using CNN with Automated Pooling

Gargi J Trivedi1*, Rajesh Sanghvi2

1Research Scholar, Department of Mathematics, G H Patel College of Engineering & Technology, CVM University, 388120, Vallabh Vidhyanagar, India
2Assistant professor, Department of Mathematics, G H Patel College of Engineering & Technology, CVM University, Vallabh Vidhyanagar, 388120, India

*Corresponding Author
Email: [email protected]

 

Received Date:07 September 2022, Accepted Date:10 October 2022, Published Date:15 November 2022

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives : The purpose of this study is to demonstrate that the most appropriate pooling is selected among different types of pooling techniques in CNN, for different applications related to image fusion. The work presents a novel method by which pooling is selected automatically as per the needs of the application. Methods: This study presents the use of multiple functions for pooling techniques which are selected automatically based on the input images to produce fused optimum output. This will make the classification process much easier and better end results are obtained. Finding: The developed automated pooling method performs better concerning processing time and produces optimal output for parameters like Peak Noise Signal Ratio, Mean Square Error, Fusion Factor, Fusion Fidelity, and Visual Information Fidelity in comparison to existing methods like Discrete Wavelet Transformation, Non-Subsampled Contourlet Transform, and Principal Component Analysis. Novelty: The novel technique presented in this paper for automated selection of pooling method provides optimal dimension reduction in both the phases of CNN, and hence allows CNN to converge faster and optimally.

Keywords: Image Fusion; CNN; Machine Learning; Pooling; Medical Image Analysis

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Copyright

© 2022 Trivedi & Sanghvi. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)

  • 15 November 2022

Year: 2022, Volume: 15, Issue: 42

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