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


Affiliations
1 Department of Electronics and Communication Engineering, Annamalai University, Annamalai Nagar 608 002, Tamil Nadu, India
2 Department of Electronics and Communication Engineering, S.R.K.R. Engineering College, Bhimavaram 534 204, Andhra Pradesh, India
 

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.

Keywords

Brain Tumor, Classification, Modified Social Group Optimization Algorithm (MSGOA), Prediction, Segmentation.
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  • Modified Social Group Optimization Based Deep Learning Techniques for Automation of Brain Tumor Detection–A Health Care 4.0 Application

Abstract Views: 50  |  PDF Views: 56

Authors

B Tapasvi
Department of Electronics and Communication Engineering, Annamalai University, Annamalai Nagar 608 002, Tamil Nadu, India
E Gnanamanoharan
Department of Electronics and Communication Engineering, Annamalai University, Annamalai Nagar 608 002, Tamil Nadu, India
N Udaya Kumar
Department of Electronics and Communication Engineering, S.R.K.R. Engineering College, Bhimavaram 534 204, Andhra Pradesh, India

Abstract


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.

Keywords


Brain Tumor, Classification, Modified Social Group Optimization Algorithm (MSGOA), Prediction, Segmentation.

References