Brief Overview of Neural Networks for Medical Applications
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 me...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2022-06-01
|
| Series: | Acta Electrotechnica et Informatica |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/aei-2022-0010 |
| _version_ | 1797848634732052480 |
|---|---|
| author | Hireš Máté Bugata Peter Gazda Matej Hreško Dávid J. Kanász Róbert Vavrek Lukáš Drotár Peter |
| author_facet | Hireš Máté Bugata Peter Gazda Matej Hreško Dávid J. Kanász Róbert Vavrek Lukáš Drotár Peter |
| author_sort | Hireš Máté |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T18:30:38Z |
| format | Article |
| id | doaj.art-30655320850449a1a6c0626c7e696dd5 |
| institution | Directory Open Access Journal |
| issn | 1338-3957 |
| language | English |
| last_indexed | 2024-04-09T18:30:38Z |
| publishDate | 2022-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Acta Electrotechnica et Informatica |
| spelling | doaj.art-30655320850449a1a6c0626c7e696dd52023-04-11T17:07:20ZengSciendoActa Electrotechnica et Informatica1338-39572022-06-01222344410.2478/aei-2022-0010Brief Overview of Neural Networks for Medical ApplicationsHireš Máté0Bugata Peter1Gazda Matej2Hreško Dávid J.3Kanász Róbert4Vavrek Lukáš5Drotár Peter6Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00Košice, Slovak Republic, Tel. +421 55 602 3175Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00Košice, Slovak Republic, Tel. +421 55 602 3175Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00Košice, Slovak Republic, Tel. +421 55 602 3175Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00Košice, Slovak Republic, Tel. +421 55 602 3175Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00Košice, Slovak Republic, Tel. +421 55 602 3175Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00Košice, Slovak Republic, Tel. +421 55 602 3175Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00Košice, Slovak Republic, Tel. +421 55 602 3175Neural 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.https://doi.org/10.2478/aei-2022-0010neural networkconvolutional neural networkimage segmentationecgu-netlstmmedical imaging |
| spellingShingle | Hireš Máté Bugata Peter Gazda Matej Hreško Dávid J. Kanász Róbert Vavrek Lukáš Drotár Peter Brief Overview of Neural Networks for Medical Applications Acta Electrotechnica et Informatica neural network convolutional neural network image segmentation ecg u-net lstm medical imaging |
| title | Brief Overview of Neural Networks for Medical Applications |
| title_full | Brief Overview of Neural Networks for Medical Applications |
| title_fullStr | Brief Overview of Neural Networks for Medical Applications |
| title_full_unstemmed | Brief Overview of Neural Networks for Medical Applications |
| title_short | Brief Overview of Neural Networks for Medical Applications |
| title_sort | brief overview of neural networks for medical applications |
| topic | neural network convolutional neural network image segmentation ecg u-net lstm medical imaging |
| url | https://doi.org/10.2478/aei-2022-0010 |
| work_keys_str_mv | AT hiresmate briefoverviewofneuralnetworksformedicalapplications AT bugatapeter briefoverviewofneuralnetworksformedicalapplications AT gazdamatej briefoverviewofneuralnetworksformedicalapplications AT hreskodavidj briefoverviewofneuralnetworksformedicalapplications AT kanaszrobert briefoverviewofneuralnetworksformedicalapplications AT vavreklukas briefoverviewofneuralnetworksformedicalapplications AT drotarpeter briefoverviewofneuralnetworksformedicalapplications |