Performance analysis of various denoising filters on intravascular ultrasound coronary artery images

Arora, Priyanka; Singh, Parminder; Girdhar, Akshay; Vijayvergiya, Rajesh

doi:10.1002/ima.22833

Cited by 3 publications

(1 citation statement)

References 74 publications

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“…Medical images [42] usually have complex structures and are highly noisy, e.g., X-rays [43,44], CT scans [45], MRIs [46], etc., which must be of high quality in diagnosis and analysis. The main application directions of graph neural networks in medical image denoising [47,48] include image noise reduction, image enhancement, motion artifact removal, data recovery, super-resolution reconstruction, and sequence image denoising. These applications will be specifically described below.…”

Section: Application Of Graph Neural Network In Medical Image Denoisingmentioning

confidence: 99%

Review of Graph Neural Networks for Medical Image

Wang

2023

EAI Endorsed Trans e-Learn

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As deep learning continues to evolve, more and more applications are generating data from non-Euclidean domains and representing them as graphs with complex relationships and interdependencies between objects. This poses a significant challenge to deep learning algorithms. Because, due to the uniqueness of graphs, applying deep learning to ubiquitous graph data is not an easy task. To solve the problem in non-Euclidean domains, graph Neural Networks (GNNs) have emerged. A graph neural network (GNN) is a neural model that captures dependencies between graphs by passing messages between graph nodes. With the continuous development of medical image technology, medical image diagnosis plays a crucial role in clinical practice. However, in practice, medical images are often affected by noise, artifacts, and other interfering factors, which may lead to inaccurate and unstable diagnostic results. Therefore, image-denoising techniques become especially critical in medical image processing. Therefore, researchers have proposed innovative methods based on graph neural networks for effective noise removal, preserving the key features of the image and improving the quality and usability of medical images. This paper reviews the research progress of graph neural networks in the field of medical image denoising. It also summarises the problems and challenges of current research and looks at the future direction of medical image-denoising research.

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Section: Application Of Graph Neural Network In Medical Image Denoisingmentioning

confidence: 99%

Review of Graph Neural Networks for Medical Image

Wang

2023

EAI Endorsed Trans e-Learn

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Lightweight Deep Learning Model Optimization for Medical Image Analysis

Al‐Milaji,

Yousif

2024

Int J Imaging Syst Tech

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Medical image labeling requires specialized knowledge; hence, the solution to the challenge of medical image classification lies in efficiently utilizing the few labeled samples to create a high‐performance model. Building a high‐performance model requires a complicated convolutional neural network (CNN) model with numerous parameters to be trained which makes the test quite expensive. In this paper, we propose optimizing a lightweight deep learning model with only five convolutional layers using the particle swarm optimization (PSO) algorithm to find the best number of kernel filters for each convolutional layer. For colored red, green, and blue (RGB) images acquired from different data sources, we suggest using stain separation using color deconvolution and horizontal and vertical flipping to produce new versions that can concentrate the representation of the images on structures and patterns. To mitigate the effect of training with incorrectly or uncertainly labeled images, grades of disease could have small variances, we apply a second‐pass training excluding uncertain data. With a small number of parameters and higher accuracy, the proposed lightweight deep learning model optimization (LDLMO) algorithm shows strong resilience and generalization ability compared with most recent research on four MedMNIST datasets (RetinaMNIST, BreastMNIST, DermMNIST, and OCTMNIST), Medical‐MNIST, and brain tumor MRI datasets.

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General Introduction

Awojoyogbe,

Dada

2024

Series in BioEngineering

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Performance analysis of various denoising filters on intravascular ultrasound coronary artery images

Cited by 3 publications

References 74 publications

Review of Graph Neural Networks for Medical Image

Review of Graph Neural Networks for Medical Image

Lightweight Deep Learning Model Optimization for Medical Image Analysis

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