Title: Detection of brain tumour using machine learning based framework by classifying MRI images

Authors: P. Nancy; G. Murugesan; Abu Sarwar Zamani; Karthikeyan Kaliyaperumal; Malik Jawarneh; Surendra Kumar Shukla; Samrat Ray; Abhishek Raghuvanshi

Addresses: Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 600001, India ' Department of Computer Engineering, Government Polytechnic College, Purasawalkam, Chennai, 600012, Tamilnadu, India ' Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, 16242, Saudi Arabia ' IT IoT – HH Campus, AMBO University, AMBO, 00251, Ethiopia ' Faculty of Computing Sciences, Gulf College, Oman ' Department of Computer Science & Engineering, SVKM'S NMIMS MPSTME, Shirpur, 405405, India ' Marketing Management, Sunstone Eduversity, Gurugram, 122002, India ' Mahakal Institute of Technology, Ujjain, 456010, India

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.

Keywords: brain tumour detection; MRI images; machine learning; Gaussian elimination; K-means; KNN; K-nearest neighbour; SVM; support vector machine; image segmentation; feature extraction; image classification; accuracy.

DOI: 10.1504/IJNT.2023.134040

International Journal of Nanotechnology, 2023 Vol.20 No.5/6/7/8/9/10, pp.880 - 896

Received: 29 Dec 2021
Received in revised form: 27 Mar 2022
Accepted: 29 Mar 2022
Published online: 10 Oct 2023 *

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