Medical Image Fusion Using CNN with Automated Pooling

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.

Comparative Study of Crank-Nicolson and Modified Crank-Nicolson Numerical methods to solve linear Partial Differential Equations

“…Solutions to such problems are not easy to find analytically; in such cases, numerical methods are useful. Many researchers have worked on various numerical methods to solve the parabolic partial differential equations, but mostly used finite difference methods (5) . There are many types of finite difference approximations used to solve parabolic equations.…”