The week's pick
Reseach Article
Brain Tumor Classification using a Support Vector Machine
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 28 |
| Year of Publication: 2022 |
| Authors: Uppala Sai Sudeep, Kandra Narasimha Naidu, Pulagam Sai Girish, Tatineni Naga Nikesh, Ch Sunanda |
10.5120/ijca2022922347
|
Uppala Sai Sudeep, Kandra Narasimha Naidu, Pulagam Sai Girish, Tatineni Naga Nikesh, Ch Sunanda . Brain Tumor Classification using a Support Vector Machine. International Journal of Computer Applications. 184, 28 ( Sep 2022), 15-17. DOI=10.5120/ijca2022922347
Abstract
A person’s life may be protected if a brain tumor is recognized early and treated effectively. The exact diagnosis of malignancies in MRI layers becomes a meticulous effort to perform, and as a consequence, the proposed method is capable of precisely classifying the tumor. Magnetic resonance imaging (MRI) is one of the most often used methods for analyzing brain tumor pictures. There are several image classification methodologies and algorithms. The purpose of machine learning and classification algorithms is to learn automatically from training and then make accurate conclusions. This study looked at the efficacy of tumor classification algorithms for categorizing MR brain image properties.During the classification process, the statistical features of the incoming images were evaluated, and the data was carefully split into multiple categories. These data were tested using SVM (support vector machines) and Logistic Regression machine learning algorithms. With a 96 percent accuracy rate, the SVM (support vector machines) technique was demonstrated to be better than other algorithms.
References
- Zacharaki et al.Classification of Brain Tumor Type and Grade Using MRI Texture and Shape in a Machine Learning Scheme,2009 Dec;62(6):1609-18.DOI: 10.1002/mrm.22147.
- R. Ezhilarasi et al.Tumor Detection in the Brain using Faster R-CNN,August 2018 Conference: 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), DOI:10.1109/I-SMAC.2018.8653705
- Li, Shutao, JT-Y. Kwok, IW-H. Tsang and Yaonan Wang. "Fusing images with different focuses using support vector machines." IEEE Transactions on neural networks15, no. 6 (2004): 1555-1561.DOI:https://doi.org/10.1109/TNN.2004.837780
- M.Gurbina, M.Lascu,”Tumor Detection and Classification of MRI images using Different Wavelets Transforms and Support Vector Machines”.Published 1 july 2019 42ndInternationalConference on Telecommunications and signal processing.DOI:10.1109/TSP.2019.8769040
- EL-syded A. El-Dahshan,” Classification using deep learning neural networks for brain tumors.Future computing and informatics Journal.” https://doi.org/10.1016/j.fcij.2017.12.001
- AMRUTA HEBLI. Dr. SUDHA GUPTA,” Brain Tumor Prediction and Classification using Support Vector Machine”. 2017 International Conference on Advances in Computing, Communication and Control (ICAC3).DOI: 10.1109/ICAC3.2017.8318767
- K. Sudharani, T. C. Sarma and K. Satya Rasad, "Intelligent Brain Tumor lesion classification and identification from MRI images using a K-NN technique," 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, 2015, pp. 777-780. DOI: 10.1109/ICCICCT.2015.7475384
- Parvathy Jyothi. Robert A Singh,” Classification of Tumors from Brain MR Slices using PCA for Discriminative Models”. International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181 Vol. 10 Issue 12, December-2021.
- Yu-Dong Zhang, LenanWu.”AnMr Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine”.January 2012Progress In Electromagnetics Research 130:369-388 DOI:10.2528/PIER12061410
- Varuna Shree, N., Kumar, T.N.R. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inf. 5, 23–30 (2018) DOI:10.1007/s40708-017-00755.
- Vinotha, K., 2014. “Brain Tumor Detection and Classification Using Histogram Equalization and Fuzzy Support Vector Machine Approach,” International Journal of Engineering and Computer Science ISSN23197242 3(5): 5823-5827.
Index Terms
Keywords