Application of Deep Learning for the diagnosis of cardiovascular diseases

Giovanah Gogi, Alexander Gegov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Significant advances in technologies have opened new possibilities for scientists to gather data in different application fields, such as medical imaging in the Healthcare Sector. Because of this, novel ideas have been generated for the development of Machine Learning (ML) Techniques. Recent research in Deep Learning (DL) has proven to transform the future of Artificial intelligence (AI). This paper provides a comprehensive survey and the application of DL techniques in diagnosing Cardiovascular Disease using biological data such as: MRI scan, CT scan, Symptoms and Family History and Blood Test results. In addition, the performances of DL techniques have been compared when applied to different datasets across several data. Finally, as this is a challenging research area, open issues and its possible future development outlooks have been discussed.
Original languageEnglish
Title of host publicationIntelligent Systems and Applications
Subtitle of host publicationProceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1
EditorsYaxin Bu, Rahul Bhatia, Supriya Kapoor
PublisherSpringer
Pages781-791
ISBN (Electronic)978-3-030-29516-5
ISBN (Print)978-3-030-29515-8
DOIs
Publication statusPublished - Sept 2019
EventIEEE SAI Intelligent Systems Conference 2019 - London, United Kingdom
Duration: 5 Sept 20196 Sept 2019
https://saiconference.com/IntelliSys
https://saiconference.com/Conferences/IntelliSys2019

Publication series

NameAdvances in Intelligent Systems and Applications
PublisherSpringer, Cham
Number1
Volume1037
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceIEEE SAI Intelligent Systems Conference 2019
Abbreviated titleIntelliSys 2019
Country/TerritoryUnited Kingdom
CityLondon
Period5/09/196/09/19
Internet address

Fingerprint

Dive into the research topics of 'Application of Deep Learning for the diagnosis of cardiovascular diseases'. Together they form a unique fingerprint.

Cite this