AlexNet – Adaptive Whale Optimization – Multiclass Support Vector Machine model for Brain Tumour Classification

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : T. Gayathri , K. Sundeep Kumar
DOI :  10.14445/22315381/IJETT-V70I5P234

Citation 

MLA Style: T. Gayathri and K. Sundeep Kumar . "AlexNet – Adaptive Whale Optimization – Multiclass Support Vector Machine model for Brain Tumour Classification." International Journal of Engineering Trends and Technology, vol. 70, no. 5, May. 2022, pp. 309-316. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P234

APA Style:T. Gayathri & K. Sundeep Kumar. (2022). AlexNet – Adaptive Whale Optimization – Multiclass Support Vector Machine model for Brain Tumour Classification. International Journal of Engineering Trends and Technology, 70(5), 309-316. https://doi.org/10.14445/22315381/IJETT-V70I5P234

Abstract
The brain tumor classification model assists doctors in deciding on the treatment of cancer. Various deep learning models for brain tumor classification are applied to improve classification accuracy. The existing models suffer from the limitation of overfitting problems in the classification. This research proposed the AlexNet – Adaptive Whale Optimization Algorithm (AWOA) – Multi-Class Support Vector Machine (MSVM) model for brain tumor classification. The AWOA method increases the exploration and exploitation that helps select features for classification. The AlexNet model consists of 8 layer that provides effective feature extraction from the input dataset. The augmentation method helps to handle the imbalanced data problem in classification due to data generation. The AlexNet – AWOA – MSVM achieves an accuracy of 99.92 %, and WOA-RBNN has 96 % accuracy in brain tumor classification.

Keywords
Adaptive Whale Optimization Algorithm, AlexNet, Augmentation, Brain tumor classification, Multi-Class Support Vector Machine.

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