Visitors: 1383

  MAZEDAN DIGITAL LIBRARY    

  JOURNAL MANAGEMENT SYSTEM

MDL
JMS  
MAZEDAN DIGITAL LIBRARY

A Review on Advance Techniques for Brain Tumor’s Detection

JOURNAL:MAZEDAN COMPUTER ENGINEERING TRANSACTIONS

Download pdf
# Authors First Online DOI Downloads Citations
1. Muhammad Khaleel Afzal 31 Mar 2021 na 18 0
Abstract

This survey paper states that Brain tumor is an abandoned growth that is affecting no of peoples all over the world. Most analysis and research tell us that numbers of death are caused because the user is not known or unaware of detection of Tumor. In this Survey paper we focus to explain about different techniques and algorithm that are used to detect Brain Tumor regarding this we will deeply discuss MRI, CT scan, Segmentation, Extraction of noisy material, K-Mean Clustering and C-Mean Fuzzy, Gaussian and Median Filters Techniques to identify the best Algorithm for Brain Tumor Detection. We will also discuss in this survey paper about what are automation based on Advance Artifical Intelligence techniques to find brain tumor without human interaction to get more accurate results. In this survey paper, we demonstrate an AI way to deal with recognize whether a MRI picture of a cerebrum contains a tumor or not. The most important concern of AIS (artificial intelligence system) is image processing machine learning and deep learning.

Keywords

Deep Learning Algorithm, GoogLeNet, Artifical Intelligence

References

  1. [1]    Access, O. (n.d.). We are IntechOpen , the world ’ s leading publisher of Open Access books Built by scientists , for scientists TOP 1 %.

    [2]    Arya, M., & Sharma, R. (2016). Brain Tumor Detection through MR Images: A Review of Segmentation Techniques. International Journal of Computer Applications, 153(7), 33–37. https://doi.org/10.5120/ijca2016912109

    [3]    Bahru, J. (2014). TUMOR BRAIN DETECTION THROUGH MR IMAGES?: A. 62(2), 387–403.

    [4]    Bauer, S., Seiler, C., Bardyn, T., Buechler, P., & Reyes, M. (2010). Atlas-based segmentation of brain tumor images using a Markov random field-based tumor growth model and non-rigid registration. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10, August 2010, 4080–4083. https://doi.org/10.1109/IEMBS.2010.5627302

    [5]    Chandra, G. R., & Rao, K. R. H. (2016). Tumor Detection in Brain Using Genetic Algorithm. Procedia Computer Science, 79, 449–457. https://doi.org/10.1016/j.procs.2016.03.058