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Modified Social Group Optimization Based Deep Learning Techniques for Automation of Brain Tumor Detection–A Health Care 4.0 Application

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Field Value
 
Title Modified Social Group Optimization Based Deep Learning Techniques for Automation of Brain Tumor Detection–A Health Care 4.0 Application
 
Creator Tapasvi, B
Gnanamanoharan, E
Kumar, N Udaya
 
Subject Brain tumor
Classification
Modified social group optimization algorithm (MSGOA)
Prediction
Segmentation
 
Description 249-254
Now-a-days, Segmentation is essential in diagnosing severe diseases wherever there is a scope for image processing. In this
work, hybridization of most popular and metaheuristic algorithms with Conventional Neural Network (CNN) has been
proposed. As a part of the study, jelly fish and Modified Social Group Optimization Algorithms (MSGOA) are used. The
CNN weights and the corresponding hyper parameters are modified or designed with the help of the respective metaheuristic
approach of the algorithm. This certainly improved the efficiency of the segmentation which is measured in several metrics
of bio-medical image processing. The accuracy, loss, Intersection over Union (IoU) are some of those metrics which are
employed in this study for better understanding of the algorithm’s effectiveness. Further the detection process is simulated
consuming 100 iterations uniformly in either of the algorithms. The proposed methodology has efficiently segmented the
tumor portion. The simulation has been carried out in MATLAB and the results are presented in terms of computed metrics,
convergence plots and segmented images.
 
Date 2023-02-08T05:08:17Z
2023-02-08T05:08:17Z
2023-02
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61359
https://doi.org/10.56042/jsir.v82i2.69936
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(02) [February 2023]