Bibliographic Details
| Title: |
Brief Overview of Neural Networks for Medical Applications |
| Authors: |
Hireš Máté, Bugata Peter, Gazda Matej, Hreško Dávid J., Kanász Róbert, Vavrek Lukáš, Drotár Peter |
| Source: |
Acta Electrotechnica et Informatica, Vol 22, Iss 2, Pp 34-44 (2022) |
| Publisher Information: |
Sciendo, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
neural network, convolutional neural network, image segmentation, ecg, u-net, lstm, medical imaging, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
Neural networks experienced great deal of success in many domains of machine intelligence. In tasks such as object detection, speech recognition or natural language processing is performance of neural networks close to that of human. This allows penetration of neural networks in many domains. The medicine is one of the domains that can successfully harvest methodological advances in neural networks. Medical personnel has to deal with huge amount of data that are used for patients’ diagnosis, monitoring and treatment. Application of neural networks in diagnosis and decision support systems have proven to add more objectivity to diagnosis, allow for quicker and more accurate decision and provide more personalized treatment. In this brief review we describe several main architectures of neural networks together with their applications. We provide description of convolutional neural networks, auto-encoders and recurrent neural networks together with their applications such as medical image segmentation, processing of electrocardiogram for arrhythmia detection and many others. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1338-3957 |
| Relation: |
https://doaj.org/toc/1338-3957 |
| DOI: |
10.2478/aei-2022-0010 |
| Access URL: |
https://doaj.org/article/30655320850449a1a6c0626c7e696dd5 |
| Accession Number: |
edsdoj.30655320850449a1a6c0626c7e696dd5 |
| Database: |
Directory of Open Access Journals |
