Medical Image Fusion Using CNN with Automated Pooling
Gargi Trivedi, Rajesh Sanghvi
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