Back propagation artificial neural network for diagnose of the heart disease

Kaur, Jagmohan; Khehra, Baljit Singh; Singh, Amarinder

doi:10.1007/s40860-022-00192-3

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…It overweights optimizations via error propagation in the neural networks. BP plays a pivotal role in enabling neural networks to recognize complex and nonlinear patterns from large datasets [29,54,55]. From the mathematical point of view, it is a calculation of the cost function, which minimizes the calculated error of the output using gradient descent or the delta rule [56].…”

Section: Backpropagation Algorithmmentioning

confidence: 99%

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Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka,

Szczepanski,

Pregowska

2024

Electronics

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Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed.

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Section: Backpropagation Algorithmmentioning

confidence: 99%

“…The key is to find a balance where neither RL nor SL overly dominates the learning process. By melding immediate feedback from supervised learning with a deep reinforcement learning strategy, ReSuMe establishes itself as a formidable tool in machine learning [55,56,58].…”

Section: Reinforcement Learning With Supervised Modelsmentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Rudnicka,

Szczepanski,

Pregowska

2024

Electronics

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“…Paper [5] is dedicated to the problem of remote collection and analysis of information about human activity for diagnostic and therapeutic purposes. The authors consider the task of human activity recognition based on signals collected by sensors of portable IoMT devices.…”

Section: Review Of the Accepted Workmentioning

confidence: 99%

Smart technologies and its application for medical/healthcare services

Izonin

Ribino

Ebrahimnejad

et al. 2023

J Reliable Intell Environ

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Section: Reinforcement Learning With Supervised Modelsmentioning

confidence: 99%

“…The Chronotron, by its essence, challenges and reshapes our understanding of how information can be encoded and processed in neural structures [50,55]. Traditional neural models have predominantly focused on the spatial domain, emphasizing the architecture and interconnections between neurons.…”

Section: Chronotronmentioning

confidence: 99%

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Rudnicka,

Szczepanski,

Pregowska

2023

Preprint

View full text Add to dashboard Cite

Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

show abstract

Back propagation artificial neural network for diagnose of the heart disease

Cited by 6 publications

References 32 publications

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview

Smart technologies and its application for medical/healthcare services

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

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