BRAIN TUMOR DETECTION SYSTEM USING CONVOLUTIONAL NEURAL NETWORK

admin October 25, 2023

Authors:

Seba Maity,Soumyadeep Jana,Sagnik Dar,Swastika Ghosh,Arijit Sai,

DOI NO:

https://doi.org/10.26782/jmcms.2023.10.00003

Keywords:

Brain tumor detection,CNN system,Tumor detection system,Image Segmentation,

Abstract

The human brain is the major controller of the humanoid system [1]. The abnormal growth and division of cells in the brain lead to a brain tumor, and the further growth of brain tumors leads to brain cancer. In the area of human health, Computer Vision plays a significant role, which reduces the human judgment that gives accurate results. CT scans, X-rays, and MRI scans are the common imaging methods among magnetic resonance imaging (MRI) that are the most reliable and secure. MRI detects every minute of objects. Our project aims to focus on the use of different techniques for the discovery of brain cancer using brain MRI. In this study, we performed pre-processing using the bilateral filter (BF) for the removal of the noises that are present in an MR image. This was followed by the binary thresholding and Convolution Neural Network (CNN) segmentation techniques for reliable detection of the tumor region [2]. Training, testing, and validation datasets are used. Based on our machine, we will predict whether the subject has a brain tumor or not. The resultant outcomes will be examined through various performance metrics that include accuracy, sensitivity, and specificity. It is desired that the proposed work would exhibit a more exceptional performance over its counterparts.

Refference:

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II. Asra Aslam, Ekram Khan, M.M. Sufyan Beg, : “Improved Edge Detection Algorithm for Brain Tumor Segmentation.” Procedia Computer Science, Volume 58, 2015, Pp 430-437, ISSN 1877-0509. 10.1016/j.procs.2015.08.057
III. B. Sathya and R. Manavalan : “Image Segmentation by Clustering Methods: Performance Analysis.” International Journal of Computer Applications (0975 – 8887) Volume 29– No.11, September 2011. 10.5120/3688-5127
IV. Seba Maity. “IMAGE WATERMARKING ON DEGRADED COMPRESSED SENSING MEASUREMENTS”. J. Mech. Cont. & Math. Sci., Vol.-18, No.-04, April (2023) pp 10-22. 10.26782/jmcms.2023.04.00002

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