Detection of Brain Tumour in MRI Scanned Images using DWT and SVM

Abstract

Abstract-detection of the tumour in a human brain is a challenging problem, due to the arrangement of the tumour cells in the brain. This paper presents an analytical method that improves the detection of brain tumour cells in its early stages and to analyze anatomical structures by classification of the samples in support vector machine and tumour cell segmentation of the sample using gray level co-occurrence matrix and extracted features. The support vector machine is used to train and classify the stage of brain tumour that would be benign, malignant or normal, and classifying the tumour for 80%. The automatic support intelligent system is proposed to detect brain tumour through the combination of discrete wavelet transform and support vector machine. This system helps to diagnose brain tumour and in vain for its treatment.

Explainable Convolutional Neural Networks for Brain Cancer Detection and Localisation

“…Babu and Varadarajan [12] explore the efficacy of gray level co-occurrence features in discerning between malignant and benign brain cancer MRIs using the Support Vector Machine classification algorithm.…”

“…Kharrat et al,in reference [16], utilise a feature set based on 2D Wavelet Transform and Spatial Gray Level Dependence Matrix to distinguish between 83 brain-cancer-affected and healthy patients. They employ the Support Vector Machine supervised machine learning algorithm for this discrimination task, similarly to researchers in references [12,14].…”

Automated brain tumor detection and classification using weighted fuzzy clustering algorithm, deep auto encoder with barnacle mating algorithm and random forest classifier techniques

“…The main advantage of MRI is that it is a noninvasive method 11 . This classification approach for classifying medical images has been used by many researchers.…”

“…The main advantage of MRI is that it is a noninvasive method. 11 This classification approach for classifying medical images has been used by many researchers. A variety of diagnostic imaging is progressively being used to classify the tumor such as MRI, X-ray, CT, positron emission tomography (PET).…”