An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gamma Distribution and Support Vector Machine
- Title
- An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gamma Distribution and Support Vector Machine
- Creator
- Aruna S.K.; Sambandam R.K.; Thaiyalnayaki S.; Vetriveeran D.
- Description
- As indicated by a measurable report distributed by the registry of central brain tumor at United States (CBTRUS), roughly 59,550 individuals were recently diagnosed to have essential benign and essential harmful brain tumors in 2017. Besides, in excess of 91,000 individuals, in the United States alone, were living with an essential harmful cerebrum tumor and 367,000 were living with an essential kind brain tumor. The task of detecting the position of the tumor in the body of the patient is the starting point for a medical treatment in the diagnosis process. The main aim of this study is to design a computer system, which is able to detect the tumor presence in the digital images of the brain in the patient and to accurately define its borderline. In this proposed model, gamma distribution method is used for training, testing, and for the feature extraction process, while SVM, support vector machine is used for the classification process. Most of the algorithms find it difficult to segment the tumors that were present in the edges. But with the help of gamma distribution along with the use of edge analysis, it is easier to identify those tumor areas that are present in the edges, thus making it easier for the preprocessing process. Gamma distribution also provides us with high accuracy, and it can also point the exact location of the tumor than compared to other algorithms. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
- Source
- Smart Innovation, Systems and Technologies, Vol-313, pp. 439-447.
- Date
- 2023-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Diagnosis process; Gamma distribution; Tumor
- Coverage
- Aruna S.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University)Kengeri Campus, Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University)Kengeri Campus, Karnataka, Bangalore, India; Thaiyalnayaki S., Department of Computer Science and Engineering, School of Computing, Bharath Institute of Higher Education and Research (Deemed to be University), Tamilnadu, Chennai, India; Vetriveeran D., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to be University)Kengeri Campus, Karnataka, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 21903018; ISBN: 978-981198668-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Aruna S.K.; Sambandam R.K.; Thaiyalnayaki S.; Vetriveeran D., “An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gamma Distribution and Support Vector Machine,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19919.