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Prediction of Neurological Disorders Using Visual Saliency: Current Trends and Future Directions

Prediction of Neurological Disorders Using Visual Saliency: Current Trends and Future Directions

Sreelakshmi S., Anoop V. S.
Copyright: © 2021 |Pages: 11
ISBN13: 9781799871880|ISBN10: 1799871886|ISBN13 Softcover: 9781799871897|EISBN13: 9781799871903
DOI: 10.4018/978-1-7998-7188-0.ch001
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MLA

S., Sreelakshmi, and Anoop V. S. "Prediction of Neurological Disorders Using Visual Saliency: Current Trends and Future Directions." Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease, edited by Manikant Roy and Lovi Raj Gupta, IGI Global, 2021, pp. 1-11. https://doi.org/10.4018/978-1-7998-7188-0.ch001

APA

S., S. & V. S., A. (2021). Prediction of Neurological Disorders Using Visual Saliency: Current Trends and Future Directions. In M. Roy & L. Gupta (Eds.), Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease (pp. 1-11). IGI Global. https://doi.org/10.4018/978-1-7998-7188-0.ch001

Chicago

S., Sreelakshmi, and Anoop V. S. "Prediction of Neurological Disorders Using Visual Saliency: Current Trends and Future Directions." In Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease, edited by Manikant Roy and Lovi Raj Gupta, 1-11. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-7188-0.ch001

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Abstract

Neurological disorders are diseases of the central and peripheral nervous system and most commonly affect middle- or old-age people. Accurate classification and early-stage prediction of such disorders are very crucial for prompt diagnosis and treatment. This chapter discusses a new framework that uses image processing techniques for detecting neurological disorders so that clinicians prevent irreversible changes that may occur in the brain. The newly proposed framework ensures reliable and accurate machine learning techniques using visual saliency algorithms to process brain magnetic resonance imaging (MRI). The authors also provide ample hints and dimensions for the researchers interested in using visual saliency features for disease prediction and detection.

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