An Effective Medical Image Segmentation of Brain Tumour using Modified CNN Algorithm
R.G. Sushmitha1, R. Muthaiah2

1R. G. Sushmitha, VLSI Design, Department of Computing, SASTRA Deemed to be University, Thanjavur (Tamil Nadu), India.
2Dr. R. Muthaiah, Department of Computing, SASTRA Deemed to be University, Thanjavur (Tamil Nadu), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 654-659 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3689048619/19©BEIESP
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Abstract: A Brain Tumour (BT) is an Unusual Proliferation of the cells inside the brain; major types of tumours are benign & malignant. Tumours can occur anywhere in the brain and contain almost any type of form, size and variance. BT is a hazardous disease that cannot be detected without hopping MRI. We present the efficient way to brainstorm the MRI films on this paper. Real datasets with different tumour shapes, sizes, locations, and internal texture are taken. Extracting the clinical data (a tumour), the aim of this paper is to come up with an effective segmentation using modified Convolution Neural Network-(CNN)where Elman network is involved. For these reasons we opt for the modified CNN based technique. It uses the MATLAB simulation.
Keyword: Brain Tumor, Benign, Malignant, CNN, and MRI.
Scope of the Article: Signal and Image Processing