ABSTRACT:
Medical sciences have a major role in every person’s life. From children to old age people, all need treatment for various diseases. Though it has a tremendous impact, it still lacks cure to many diseases, one of which is brain tumor. Brain tumor can be dangerous if it is a malignant tumor or secondary tumor and needed proper evaluation for their treatment. Clinically, the efficiency and accuracy are important in such treatment. For this purpose, automated brain tumor detection can be use, where we can detect and classify the tumor without human intervention. Brain tumor detection and classification is an important research area that still lacks efficiency because of variations of tissue characteristics inside the brain like shape, size, location, etc. Many people are living with a brain tumor which is expected to be cured soon through diagnosis. Taking into account the importance of detection of brain tumor, this paper is focused on the classification of brain MRI images into tumorous or non-tumorous images. It also provides a comparison of different existing classification techniques using GLCM features and hybridization of GLCM and DWT features. Performance is evaluated using metrics like accuracy, precision, recall and Fmeasure. From our observation, we found that using hybridized GLCM and DWT feature, result is better than that of GLCM feature for most methodologies.