The week's pick
Random Articles
Reseach Article
Brain Tumor MRI Image Processing and Classification by Edge Detection using ML Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 13 |
| Year of Publication: 2022 |
| Authors: Meetali, R.M. Samant, Parimal Bartakke, Jayesh Mohite, Subhasini Priya |
10.5120/ijca2022922132
|
Meetali, R.M. Samant, Parimal Bartakke, Jayesh Mohite, Subhasini Priya . Brain Tumor MRI Image Processing and Classification by Edge Detection using ML Algorithms. International Journal of Computer Applications. 184, 13 ( May 2022), 55-59. DOI=10.5120/ijca2022922132
Abstract
A tumor on a brain is an abnormal growth of cells in the brain, which may turn into a malignant tumor and can become fatal as per the studies suggested by various institutes such as Brain Tumor Epidemiology: Consensus from the Brain Tumor Epidemiology Consortium (The University of Texas), etc. The major problem with a brain tumor is specifying its location, shape, and size. Despite many efforts and promising results in this field of tumor detection, accurate classification from type benign to type malignant is still challenging. One of the most common methods of diagnosing brain tumors is Magnetic Resonance Imaging (MRI) but its accuracy is not very high. The proposed system suggests a novel method for Brain Tumor Detection and Classification by using some of the prominent Machine Learning (ML) based algorithms such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). This approach is to separate images from an MRI that can be classified as type benign or type malignant. In this experimentation, K-NN has shown promising classification accuracy of 89%.
References
- D. Suresha and N. Jagadisha , “ Detection of Brain Tumor using Image Processing”, Fourth International Conference on Computing Methodologies and communication, 2020
- Ashfaq Hussain and Ajay Khunteta,” Semantic segmentation of brain tumor from MRI images and SVM Classification using GLCM features”, Second International Conference on Inventive Research in Computing Application, 2020
- S. Suhas and C. R. Venugopal, “MRI image preprocessing and noise removal technique using linear and nonlinear filters”, 2017 International Conference on Electrical , Electronics , Communication ,Computer and Optimization Techniques
- N. Varuna Shree and T. N. R Kumar, “Identification and classification of brain tumor MRI images with feature extraction using DWT and Probabilistic neural network”, Springer , 2018
- F. P. Polly and S.K . Shil, “ Detection and classification of HGG and LGG brain tumor using machine learning ”, International Conference on Information Networking , 2018
- N. B. Bahadure, A. K. Ray and H. P. Thethi ,” Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM”, 2017.
- Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi , Daniel L. Rubin and Bradley J. Erickson, “Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions” J Digit Imaging DOI 10.1007/s10278-017- 9983-4, 2017
- Israel D. Gebru, Xavier Alameda-Pineda, Florence Forbes and Radu Horaud, “EM Algorithms for Weighted-Data Clustering with Application to Audio-Visual Scene Analysis “ IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. xx, no. y, 2016.
- Cuadra, M.B., Gomez, J., Hagmann, P., Pollo, C., Villemure, J.-G., Dawant, B.M., Thiran, J.-Ph., 2002. Atlas-based segmentation of pathological brains using a model of tumor growth. In: Dohi, T., Kikinis, R. (Eds), Medical Image Computing and Computer-Assisted Intervention MICCAI 2002, Springer, pp. 380-387.
- Clark, M.C., Hall, L.O., Goldgof, D.B., Velthuizen, R., Murtagh, F.R., Silbiger, M.S., 1998. Automatic tumor-segmentation using knowledge-based techniques. IEEE Transactions on Medical Imaging 117, 187-201.
- Fletcher-Health, L.M., Hall, L.O., Goldgof, D.B., Murtagh, F.R., 2001. Automatic segmentation if non-enhancing brain tumors in 282 M. Prastawa et al. / Medical Image Analysis 8 (2004)275-283 magnetic resonance images. Artificial Intelligence in Medicine 21, 43-63.
- Marcel Prastawa, Elizabeth Bullitt, Sean Ho, Guido Gerig “A brain tumor segmentation framework based on outlier detection”. Medical Image Analysis 8 (2004) 275-283 © Elsevier.
- Khan M. Iftekharuddin, Jing Zheng, Mohammad A. Islam, Robert J. Ogg. “Fractal-based brain tumor detection in multimodal MRI” Appl. Math. Comput. (2008), doi:10.1016/j.amc.2007.10.063
- Andrews DW, Scott CB, Sperduto PW, et al. Whole brain radiation therapy with or without stereotactic radiosurgery boost for patients with one to three brain metastases: phase III results of the RTOG 9508 randomized trial. Lancet 2004;363:1665–1672.
- Patchell RA, Tibbs PA, Walsh JW, et al. A randomized trial of surgery in the treatment of single metastases to the brain. N Engl J Med 1990;322:494-500.
- Sanghavi SN, Miranpuri SS, Chappell R, et al. Radiosurgery with patients with brain metastases: a multi-institutional analysis, stratified by the RTOG recursive partitioning analysis method. J. Radiat Oncol Biol Phys 2001;52:426–434.
- Pollock BE, Brown PD, Foote RL, et al. Properly selected patients with multiple brain metastases may benefit from aggressive treatment of their intracranial disease. J Neuro oncol 2003;61:73-80.
- Sneed PK, Suh JH, Goetsch SJ, et al. A multi-institutional review of radiosurgery alone vs. radiosurgery with whole brain radiotherapy as the initial management of brain metastases. Int J Radiat Oncol Biol Phys 2002;53:519-526.
- Smith ML, Lee JYK. Stereotactic radiosurgery in the management of brain metastasis. Neurosurg Focus 2007;22:E5.
- P. Tamije Selvy, V. Palanisamy, T. Purusothaman. “Performance Analysis of Clustering Algorithm in Brain Tumor Detection of MRI Images”. European Journal of Scientific Research ISSN 1450-216X Vol.62 No.3 (2011), pp 321-330.
Index Terms
Keywords