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Saravanan S, Kumar VV, Sarveshwaran V, Indirajithu A, Elangovan D, Allayear SM. Computational and Mathematical Methods in Medicine Glioma Brain Tumor Detection and Classification Using Convolutional Neural Network. Comput Math Methods Med 2022; 2022:4380901. [PMID: 36277002 PMCID: PMC9586767 DOI: 10.1155/2022/4380901] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 09/29/2023]
Abstract
The classification of the brain tumor image is playing a vital role in the medical image domain, and it directly assists the clinicians to understand the severity and to take an appropriate solution. The magnetic resonance imaging tool is used to analyze the brain tissues and to examine the different portion of brain circumstance. We propose the convolutional neural network database learning along with neighboring network limitation (CDBLNL) technique for brain tumor image classification in medical image processing domain. The proposed system architecture is constructed with multilayer-based metadata learning, and they have integrated with CNN layer to deliver the accurate information. The metadata-based vector encoding is used, and the type of coding estimation for extra dimension is known as sparse. In order to maintain the supervised data in terms of geometric format, the atoms of neighboring limitation are built based on a well-structured k-neighbored network. The resultant of the proposed system is considerably strong and subjective for classification. The proposed system used two different datasets, such as BRATS and REMBRANDT, and the proposed brain MRI classification technique outcome is more efficient than the other existing techniques.
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Affiliation(s)
- S. Saravanan
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
| | - V. Vinoth Kumar
- Department of Computer Science and Engineering, Jain (Deemed to Be University), Bangalore, India
| | - Velliangiri Sarveshwaran
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India
| | - Alagiri Indirajithu
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014 Tamil Nadu, India
| | - D. Elangovan
- Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
| | - Shaikh Muhammad Allayear
- Department of Multimedia and Creative Technology, Daffodil International University, Daffodil Smart City, Khagan, Ashulia, Dhaka, Bangladesh
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Venkatasen M, Mathivanan SK, Jayagopal P, Mani P, Rajendran S, Subramaniam U, Ramalingam AC, Rajasekaran VA, Indirajithu A, Sorakaya Somanathan M. Forecasting of the SARS-CoV-2 epidemic in India using SIR model, flatten curve and herd immunity. J Ambient Intell Humaniz Comput 2020:1-9. [PMID: 33224306 PMCID: PMC7666824 DOI: 10.1007/s12652-020-02641-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/24/2020] [Indexed: 05/21/2023]
Abstract
In this paper, we are presenting an epidemiological model for exploring the transmission of outbreaks caused by viral infections. Mathematics and statistics are still at the cutting edge of technology where scientific experts, health facilities, and government deal with infection and disease transmission issues. The model has implicitly applied to COVID-19, a transmittable disease by the SARS-CoV-2 virus. The SIR model (Susceptible-Infection-Recovered) used as a context for examining the nature of the pandemic. Though, some of the mathematical model assumptions have been improved evaluation of the contamination-free from excessive predictions. The objective of this study is to provide a simple but effective explanatory model for the prediction of the future development of infection and for checking the effectiveness of containment and lock-down. We proposed a SIR model with a flattening curve and herd immunity based on a susceptible population that grows over time and difference in mortality and birth rates. It illustrates how a disease behaves over time, taking variables such as the number of sensitive individuals in the community and the number of those who are immune. It accurately model the disease and their lessons on the importance of immunization and herd immunity. The outcomes obtained from the simulation of the COVID-19 outbreak in India make it possible to formulate projections and forecasts for the future epidemic progress circumstance in India.
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Affiliation(s)
- Maheshwari Venkatasen
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu India
| | - Sandeep Kumar Mathivanan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu India
| | - Prabhu Jayagopal
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu India
| | - Prasanna Mani
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu India
| | - Sukumar Rajendran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu India
| | - UmaShankar Subramaniam
- Renewable Energy Lab, Department of Communication and Networks, College of Engineering, Prince Sultan University, Riyadh, Saudi Arabia
| | | | - Vijay Anand Rajasekaran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu India
| | - Alagiri Indirajithu
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu India
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