Performance evaluation of optimised SVM for classification of brain tumour
by Arun Kumar; M.A. Ansari; Alaknanda Ashok
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 14, No. 5, 2022

Abstract: In today's scenario, machine learning tools are most widely used for the classification of images in the field of medical science. Support vector machine (SVM) is one of the most popular and highly used for such classifications. Further, such classifications are highly related to the number of features selected from any medical image. The computation time and the memory required for the successful implementation of any classification tool is directly dependent on the number of features. So, in order to get the more accurate classification results, the features of the medical image must be optimised. The present study mainly aims on the development of an improved classification technique by combining some optimisation approaches. In this study, SVM is implemented for the classification of the brain tumour by optimising the features of the magnetic resonance imaging (MRI) using three different optimisation approaches namely, particle swarm optimisation, grey wolf optimisation and firefly algorithm. The results obtained from this study depict that SVM along with the grey wolf optimisation provides more accurate classification of the brain tumour with an accuracy of 96.8% ± 2 in comparison to particle swarm optimisation and firefly optimisation with an accuracy of 93% ± 2 and 85% ± 5 respectively.

Online publication date: Wed, 07-Sep-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Medical Engineering and Informatics (IJMEI):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com