Prediction of Neurological Disorders using Deep Learning: A Review

Akhilesh Kumar Tripathi, Arvind Kumar Tiwari

2021

Abstract

Artificial intelligence (AI) is a field of computer science that is efficiently as well as effectively used to analyze composite health data and extract key association in datasets. Deep learning methods are field of machine learning method that has received important consideration in methodical society. It varies from straightforward machine learning techniques by desirable quality to study the most favorable illustration from untreated data. Given its capability to find abstract along with intricate patterns, deep learning has been functional in field of neuroimaging analysis of neurological diseases featured by delicate as well as disperse changes. This paper presents a key aspect of deep learning along with review various past work that have been used to move toward a different machine learning algorithms to forecast the neurological disorders.

Paper Citation

in Harvard Style

Tripathi A. and Tiwari A. (2021). Prediction of Neurological Disorders using Deep Learning: A Review. In Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE, ISBN 978-989-758-544-9, pages 135-139. DOI: 10.5220/0010564100003161

in Bibtex Style

@conference{icacse21,
author={Akhilesh Kumar Tripathi and Arvind Kumar Tiwari},
title={Prediction of Neurological Disorders using Deep Learning: A Review},
booktitle={Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE,},
year={2021},
pages={135-139},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010564100003161},
isbn={978-989-758-544-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering - Volume 1: ICACSE,
TI - Prediction of Neurological Disorders using Deep Learning: A Review
SN - 978-989-758-544-9
AU - Tripathi A.
AU - Tiwari A.
PY - 2021
SP - 135
EP - 139
DO - 10.5220/0010564100003161