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Fusing Medical Images Using Pyramid Decomposition by DLCNN Method

Fusing Medical Images Using Pyramid Decomposition by DLCNN Method
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Author(s): L. K. Hema (Aarupadai Veedu Institute of Technology, India), Rajat Kumar Dwibedi (Aarupadai Veedu Institute of Technology, India), V. Vanitha (Aarupadai Veedu Institute of Technology, India), Animesh Chandra Dey (Aarupadai Veedu Institute of Technology, India), G. R. Jothlakshmi (Vels Institute of Science, Technology, and Advanced Studies, India)and Sanat Kumar Dwibedi (Orissa University of Agriculture and Technology, India)
Copyright: 2024
Pages: 12
Source title: Advancements in Clinical Medicine
Source Author(s)/Editor(s): P. Paramasivan (Dhaanish Ahmed College of Engineering, India), S. Suman Rajest (Dhaanish Ahmed College of Engineering, India), Karthikeyan Chinnusamy (Veritas, USA), R. Regin (SRM Instıtute of Science and Technology, India)and Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand)
DOI: 10.4018/979-8-3693-5946-4.ch003


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Clinical imaging is an essential component in a wide variety of restorative examinations and therapies nowadays, according to the most recent developments in logic. Unless otherwise specified, the process of intertwining clinical photos can be considered one of the most effective methods for combining many distinct modular pictures through the utilisation of picture handling technologies. This work presents a three-layered crossbreed combination statement that is created by combining the Laplacian mode pyramid and the Gaussian mode pyramid decay into the brought picture and performing at first followed by the age of weight-based convolution brain organisations (CNN) approach. The goal of this work is to overcome the disadvantage of compelling pictures by conveying viable quality pictures and the rousted merged pictures that have been flopped by pre-customary methodologies.

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