Abstract
The abnormal growths of cells in the brain are called tumours and cancer is a term used to represent malignant tumours. Usually, CT or MRI scans are used for the detection of cancer regions in the brain. Positron Emission Tomography, Cerebral Arteriogram, Lumbar Puncture, Molecular testing are also used for brain tumour detection. In this study, MRI scan images are taken to analyse the disease condition. Objective this research works is i) identify the abnormal image ii) segment tumour region. Density of the tumour can be estimated from the segmented mask and it will help in therapy. Deep learning technique is employed to detect abnormality from MRI images. Multi-level thresholding is applied to segment the tumour region. Number of malignant pixels gives the density of the affected region. The objective of the research is to find the tumour portion of the abnormal MRI brain image using automatic segmentation. The automatic segmentation is accomplished using wavelet transform to extract various features. Then the abnormal images are processed by conventional K means clustering and Fuzzy C means algorithm. But the conventional methods take long iteration and long time to converge. The hybrid clustering method overcomes the long iteration and time.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Irish Interdisciplinary Journal of Science & Research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
