Title: Automatic detection classification and area calculation of brain tumour in MRI using wavelet transform and SVM classifier

Authors: Nitish; Amit Kumar Singh

Addresses: ICE Department, Dr. B.R. Ambedkar NIT Jalandhar, Punjab, India ' ICE Department, Dr. B.R. Ambedkar NIT Jalandhar, Punjab, India

Abstract: From last two decade automatic detection of brain tumour in MR images is an emerging area of research in medical science. A brain tumour is an abnormal mass of tissues in which cells grow and multiply uncontrollably, seemingly unchecked by the mechanisms that control normal cells. A brain tumour is the second leading cause of cancer-related deaths in men and the fifth leading cause among women. Diagnosis of the tumour at earlier stage is a very important part of its treatment. For detecting the brain tumour MRI and CT scan are the most significant techniques. Despite promising techniques like MRI, CT scan, characterisation of abnormalities is still a challenging and difficult task. This paper presented an automated computer-aided brain tumour detection system which first classifies the tumourous from non-tumourous MRI images and then calculates the area of the affected region using hysteresis thresholding technique which segments the lesion region from other part, feature extraction using Gabor filters and then SVM classifier for classification. The pursuance of the suggested system is evaluated in term of sensitivity, specificity, classification accuracy and area under curve (AUC) using ground truth images. The proposed system gives an average accuracy of 97%.

Keywords: magnetic resonance imaging; MRI; tumour; hysteresis thresholding; wavelet transform; Gabor filters; dimensionality reduction; classification; confusion matrix; support vector machine; SVM; sensitivity; specificity; receiver operating characteristic; ROC; area under curve; AUC.

DOI: 10.1504/IJISTA.2020.112433

International Journal of Intelligent Systems Technologies and Applications, 2020 Vol.19 No.6, pp.526 - 540

Received: 25 Apr 2019
Accepted: 18 Jan 2020
Published online: 15 Jan 2021 *

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