Modified CNN Architecture for Efficient Classification of Glioma Brain Tumour

Abstract

Magnetic Resonance Imaging (MRI) is the medical imaging modality that provides more useful functional data for the diagnosis of pathological conditions in brain tumour than any other modality. Manual observation of MRI data to diagnose the tumour is time-consuming and hence the objective of this work is to classify the Glioma brain tumour using a Convolutional Neural Network (CNN). This proposed work aims to design a new model of the modified CNN architecture for the classification of Gliomas. Various processes were used for the classification of MRI brain tumours, which include image pre-processing, image feature extraction, and subsequent classification of Glioma brain tumours. The proposed modified CNN obtained high classification accuracy of 94.65% compared to the pre-trained AlexNet Model. The traditional machine learning techniques like Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) gain an accuracy of 86.1% and 66.7%, respectively.

Keywords:

• Convolutional neural networks
• Feature extraction
• Glioma brain tumour
• Image classification
• SVM
• Traditional machine learning

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

J. Angelin Jeba

J Angelin Jeba received a BE degree specialization in electronics and communication engineering from Anna University, Chennai, in the year 2008 and an ME degree specialization in applied electronics from the same University, in the year 2011, where she is currently pursuing full-time research in the field of medical image processing at the Department of Electronics and Communication Engineering. She has around 3+ years of teaching experience in the reputed engineering colleges in Chennai. She has published research papers in various international, national conferences and in journals. Corresponding author. Email: [email protected]

S. Nirmala Devi

S Nirmala Devi Devi received her PhD degree in medical image processing from the Faculty of Information and Communication Engineering, College of Engineering, Anna University in the year 2009. The title of her research is segmentation and 3D visualization of stenosis in X-ray angiogram images. Her research interests are medical image processing, advanced neural computing, and physiological modelling. She has around 25+ years of teaching experience, and currently she is working as a professor in the Department of ECE, Anna University, Chennai. She has published around 25 research papers in reputed journals and in various international and national conferences. Email: [email protected]

M. Meena

M Meena received her BE degree specialization in electronics and communication engineering from Anna University, Chennai, in the year 2017 and ME degree with specialization in medical electronics from Anna University, Chennai, during the year 2019. Email: [email protected]