Detection of brain tumour using machine learning based framework by classifying MRI images
by P. Nancy; G. Murugesan; Abu Sarwar Zamani; Karthikeyan Kaliyaperumal; Malik Jawarneh; Surendra Kumar Shukla; Samrat Ray; Abhishek Raghuvanshi
International Journal of Nanotechnology (IJNT), Vol. 20, No. 5/6/7/8/9/10, 2023

Abstract: The fatality rate has risen in recent years due to an increase in the number of encephaloma tumours in each age group. Because of the complicated structure of tumours and the involution of noise in magnetic resonance (MR) imaging data, physical identification of tumours becomes a difficult and time-consuming operation for medical practitioners. As a result, recognising and locating the tumour's location at an early stage is crucial. Cancer tumour areas at various levels may be followed and prognosticated using medical scans, which can be utilised in concert with segmentation and relegation techniques to provide a correct diagnosis at an early time. This paper aims to develop image processing and machine learning based framework for early and accurate detection of brain tumour. This framework includes image preprocessing, image segmentation, feature extraction, and classification using the support vector machine (SVM), K-nearest neighbour (KNN), and Naïve Bayes algorithms. Image preprocessing is performed using Gaussian Elimination, image enhancement using histogram equalisation, image segmentation using k-means and feature extraction performed using PCA algorithm. For performance comparison, parameters like: accuracy, sensitivity and specificity are used. Experimental results have shown that the KNN is getting better accuracy for classification of brain tumour related images. KNN is performing admirably in terms of accuracy. In terms of specificity, both SVM and KNN perform similarly well. KNN outperforms other algorithms in terms of sensitivity. Accuracy of KNN classifier is around 98% in brain tumour image classification.

Online publication date: Tue, 10-Oct-2023

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 Nanotechnology (IJNT):
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