A Survey On Various Architectures, Models And Methodologies For Identifying Brain Tumor
 |
International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2019 by IJCTT Journal | ||
| Volume-67 Issue-7 |
||
| Year of Publication : 2019 | ||
| Authors : Divya D J , Prakasha S | ||
| DOI : 10.14445/22312803/IJCTT-V67I7P110 |
MLA Style: Divya D J , Prakasha S"A Survey On Various Architectures, Models And Methodologies For Identifying Brain Tumor" International Journal of Computer Trends and Technology 67.7 (2019): 62-67.
APA Style Divya D J , Prakasha S. A Survey On Various Architectures, Models And Methodologies For Identifying Brain Tumor International Journal of Computer Trends and Technology, 67(7),62-67.
Abstract
Digital image processing in computer science is using computer algorithms to perform digital image processing operations. Digital image processing has many advantages over analog image processing as a subcategory or field of digital signal processing. It allows the input data to be applied to a much wider range of algorithms. Image processing in the medical field focuses on image capture for both diagnostic and therapeutic purposes, including the analysis, enhancement and display of images captured through X-ray, ultrasound, MRI, nuclear medicine and optical imaging technologies. Image processing and analysis can be used to determine the size, volume and vasculature of a tumor or organ; flow parameters of blood or other fluids; and microscopic changes that have yet to raise any otherwise noticeable problems. A brain tumor is a mass or growth of your brain`s abnormal cells. There are two main types of tumors: cancerous (malignant) tumors and benign tumors.[2] Cancerous tumors can be divided into principal tumors that begin in the brain and secondary tumors that have spread elsewhere, known as tumors of brain metastasis. All brain tumor types can give rise to symptoms that vary depending on the part of the brain involved. Using fuzzy logic, our proposed methodology for identifying the brain tumor is established as a fuzzy inference system. Because fuzzy logic deals with uncertainty, its application is adequate and more effective in this area. Fuzzy C-means clustering algorithm along with self-organizing neural MAP network along with threshold and structure and function for proper medical data identification. First, to calculate the portion where the brain tumor is present, then identify all the parameter related to it. This methodology provides a more effective way of detecting the brain tumor and helps bring the importance of computing to the medical field that helps doctors find the tumor very easily and as quickly as possible.
Reference
[1] J. Patel and K. Doshi, ?A Study of Segmentation Methods for Detection of Tumor in Brain MRI,? Adv. Electron. Electr. Eng., vol. 4, no. 3, pp. 279–284, 2014.
[2] G. Conway, ?Novel Therapeutic Approaches to Induce Autophagy in Brain Cancer Novel Therapeutic Approaches to Induce Autophagy in Brain Cancer Food Science and Environmental Health , FOCAS Research Supervisors : Dr James F . Curtin , Dr Orla Howe,? 2016.
[3] M. Sharma, ?Brain Tumor Segmentation using hybrid Genetic Algorithm and Artificial Neural Network Fuzzy Inference System (ANFIS),? Int. J. Fuzzy Log. Syst., vol. 2, no. 4, pp. 31–42, 2012.
[4] B. Nisha and V. J. M, ?A review on brain tumor segmentation techniques,? vol. 4, no. 5, pp. 262–265, 2018.
[5] P. Ebenezer, H. Nunoo-Mensah, R. Junior, and J. Raissa, ?Brain Tumor Segmentation using SLIC Superpixels and Optimized Thresholding Algorithm,? Int. J. Comput. Appl., vol. 181, no. 20, pp. 1–5, 2018.
[6] H. Khotanlou, O. Colliot, J. Atif, I. Bloch, H. Khotanlou, O. Colliot, J. Atif, and I. Bloch, ?3D brain tumor segmentation in MRI using fuzzy classification , symmetry analysis and spatially constrained deformable models To cite this version : HAL Id : hal-01251278 3D Brain Tumor Segmentation in MRI Using Fuzzy Classification , Symmetry Analysis an,? pp. 0–25, 2016.
[7] T. Kalaiselvi, P. Sriramakrishnan, and P. Nagaraja, ?Brain Tumor Boundary Detection by Edge Indication Map Using Bi-Modal Fuzzy Histogram Thresholding Technique from MRI T2-Weighted Scans,? Int. J. Image, Graph. Signal Process., vol. 8, no. 9, pp. 51–59, 2015.
[8] N. Zhang, ?Feature selection based segmentation of multi-source images : application to brain tumor segmentation in multi-sequence MRI To cite this version : HAL Id : tel-00701545 Application to Brain Tumor Segmentation,? 2012.
[9] T. Logeswari and M. Karnan, ?An enhanced implementation of brain tumor detection using segmentation based on soft computing,? 2010 Int. Conf. Signal Acquis. Process. ICSAP 2010, vol. 9, no. 2, pp. 243–247, 2010.
[10] B. Sharma and B. M. Singh, ?Review Paper on Brain Tumor Detection Using Pattern Recognition Techniques,? vol. 3, no. 2, pp. 151–156, 2016.
[11] R. Balu, ?Design and development of automatic appendicitis detection system using sonographic image mining,? Shodhganga a Reserv. Indian theses @ INFLIBNET, p. 167, 2015.
[12] S. Safa, B. Bokharaeian, and A. Soleymani, ?Brain MR Segmentation through Fuzzy Expectation Maximization and Histogram Based K-Means,? Int. J. Comput. Electr. Eng., vol. 5, no. 5, pp. 438–441, 2013.
[13] M. N. Tahir, ?Classification and characterization of brain tumor MRI by using gray scaled segmentation and DNN,? no. May, 2018.
[14] A. Hasan, F. Meziane, R. Aspin, and H. Jalab, ?Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge,? Symmetry (Basel)., vol. 8, no. 11, p. 132, 2016.
Keywords
Identifying Brain Tumor ,Benign Tumors ,Malignant tumor