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Haar Adaptive Taylor-ASSCA-DCNN: A Novel Fusion Model for Image Quality Enhancement

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Field Value
 
Title Haar Adaptive Taylor-ASSCA-DCNN: A Novel Fusion Model for Image Quality Enhancement
 
Creator Singh, Vineeta
Kaushik, Vandana Dixit
 
Subject Correlation-based weighted model
Deep model
Haar wavelet
Magnetic resonance imaging (MRI)
Medical image fusion
 
Description 568-578
In medical imaging, image fusion has a prominent exposure in extracting complementary information out of varying
medical image modalities. The utilization of different medical image modality had imperatively improved treatment
information. Each kind of modality contains specific data regarding subject being imaged. Various techniques are devised
for solving the issue of fusion, but the major issue of these techniques is key features loss in fused image, which also leads
to unwanted artefacts. This paper devises an Adaptive optimization driven deep model fusing for medical images to obtain
the essential information for diagnosis and research purpose. Through our proposed fusion scheme based on Haar wavelet
and Adaptive Taylor ASSCA Deep CNN we have developed fusion rules to amalgamate pairs of Magnetic Resonance
Imaging i.e. MRI like T1, T2. Through experimental analysis our proposed method shown for preserving edge as well as
component related information moreover tumour detection efficiency has also been increased. Here, as input, two MRI
images have been considered. Then Haar wavelet is adapted on both MRI images for transformation of images in low as
well as high frequency sub-groups. Then, the fusion is done with correlation-based weighted model. After fusion, produced
output is imposed to final fusion, which is executed through Deep Convolution Neural Network (DCNN). The Deep CNN is
trained here utilizing Adaptive Taylor Atom Search Sine Cosine Algorithm (Adaptive Taylor ASSCA). Here, the Adaptive
Taylor ASSCA is obtained by integrating adaptive concept in Taylor ASSCA. The highest MI of 1.672532 have been
attained using db2 wavelet for image pair 1, highest PSNR 42.20993dB using db 2 wavelet for image pair 5 and lowest
RMSE 5.204896 using sym 2 wavelet for image pair 5, have been shown proposed Adaptive Taylor ASO + SCA-based
Deep CNN.
 
Date 2023-05-09T08:20:24Z
2023-05-09T08:20:24Z
2023-05
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61857
https://doi.org/10.56042/jsir.v82i05.1095
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(05) [May 2023]