<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Brief Overview of Neural Networks for Medical Applications

Department of Computers and Informatics, Faculty of Electrical Engineering and Informatics

Acta Electrotechnica et Informatica

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"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...

Brief Overview of Neural Networks for Medical Applications

Data publikacji: 12 sie 2022

Zakres stron: 34 - 44

Otrzymano: 20 kwi 2022

Przyjęty: 21 cze 2022

DOI: https://doi.org/10.2478/aei-2022-0010

Słowa kluczowe
neural network, convolutional neural network, image segmentation, ECG, U-NET, LSTM, medical imaging

© 2022 Máté Hireš et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Brief Overview of Neural Networks for Medical Applications

Data publikacji: 12 sie 2022

Zakres stron: 34 - 44

Otrzymano: 20 kwi 2022

Przyjęty: 21 cze 2022

DOI: https://doi.org/10.2478/aei-2022-0010

Słowa kluczoweneural network, convolutional neural network, image segmentation, ECG, U-NET, LSTM, medical imaging

© 2022 Máté Hireš et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
neural network, convolutional neural network, image segmentation, ECG, U-NET, LSTM, medical imaging