Record Details

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

Online Publishing @ NISCAIR

View Archive Info
 
 
Field Value
 
Authentication Code dc
 
Title Statement <p>Modified Social Group Optimization Based Deep Learning Techniques for Automation of Brain Tumor Detection–A Health Care 4.0 Application</p>
 
Added Entry - Uncontrolled Name Tapasvi, B ; Department of Electronics and Communication Engineering, Annamalai University, Annamalai Nagar 608 002, Tamil Nadu, India
Gnanamanoharan, E ; Department of Electronics and Communication Engineering, Annamalai University, Annamalai Nagar 608 002, Tamil Nadu, India
Kumar, N Udaya; Department of Electronics and Communication Engineering, S.R.K.R. Engineering College, Bhimavaram 534 204, Andhra Pradesh, India
 
Uncontrolled Index Term Brain tumor, Classification, Modified social group optimization algorithm (MSGOA), Prediction, Segmentation
 
Summary, etc. <p>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.</p>
 
Publication, Distribution, Etc. Journal of Scientific & Industrial Research
2023-02-09 21:08:13
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/69936
 
Data Source Entry Journal of Scientific & Industrial Research; ##issue.vol## 82, ##issue.no## 02 (2023)
 
Language Note en