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.
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More From: Irish Interdisciplinary Journal of Science & Research
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