Automated neurological brain disease detection in magnetic resonance imaging using deep learning approaches
- Title
- Automated neurological brain disease detection in magnetic resonance imaging using deep learning approaches
- Creator
- Thilagavathi S.; Sridhar D.; Jawahar S.
- Description
- A neurological type of brain disease called multiple sclerosis (MS) impairs how well the nervous system is able to function efficiently and causes people to experience visual, sensory, and problems with movement. Multiple methods of detection have been proposed so far for diagnosing MS; among them, magnetic resonance imaging (MRI) has drawn a lot of interest from healthcare providers. The ability to quickly diagnose lesions related to MS depends on a fundamental understanding of the anatomy and workings of the brain that MRI technology provides doctors. Using an MRI for diagnosing MS is tedious, time-consuming, and prone to human error. In the present investigation, lesion activity involves preprocessing and segmentation of the MS images from two time points using deep learning approaches. 2024 by IGI Global. All rights reserved.
- Source
- Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases, pp. 150-178.
- Date
- 2024-01-01
- Publisher
- IGI Global
- Coverage
- Thilagavathi S., Department of Computer Science, Sri Krishna Adithya College of Arts and Science, Coimbatore, India; Sridhar D., School of Computer Science, Dr. Vishwanath Karad MIT World Peace University, Pune, India; Jawahar S., School of Sciences, CHRIST Deemed to be University, Ghaziabad, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-836931282-7; 979-836931281-0
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Thilagavathi S.; Sridhar D.; Jawahar S., “Automated neurological brain disease detection in magnetic resonance imaging using deep learning approaches,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/17821.