A robust deformed convolutional neural network (CNN) for image denoising
Due to strong learning ability, convolutional neural networks (CNNs) have been developed
in image denoising. However, convolutional operations may change original distributions of …
in image denoising. However, convolutional operations may change original distributions of …
Artificial intelligence in cardiac computed tomography
AA Aromiwura, T Settle, M Umer, J Joshi… - Progress in …, 2023 - Elsevier
Artificial Intelligence (AI) is a broad discipline of computer science and engineering. Modern
application of AI encompasses intelligent models and algorithms for automated data …
application of AI encompasses intelligent models and algorithms for automated data …
Particle swarm optimization in biomedical technologies: innovations, challenges, and opportunities
K Suriyan, R Nagarajan - … for Health Literacy and Medical Practice, 2024 - igi-global.com
This chapter explores particle swarm optimization (PSO) in the rapidly evolving landscape of
biomedical technologies. The study begins by introducing the fundamental principles of …
biomedical technologies. The study begins by introducing the fundamental principles of …
[HTML][HTML] NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing
Diffusion-weighted magnetic resonance imaging (dMRI) has found great utility for a wide
range of neuroscientific and clinical applications. However, high-resolution dMRI, which is …
range of neuroscientific and clinical applications. However, high-resolution dMRI, which is …
[HTML][HTML] Removal of speckle noises from ultrasound images using five different deep learning networks
O Karaoğlu, HŞ Bilge, I Uluer - Engineering Science and Technology, an …, 2022 - Elsevier
Image enhancement methods are applied to medical images to reduce the noise that they
contain. There are many academic studies in the literature using classical image …
contain. There are many academic studies in the literature using classical image …
A wavelet denoising approach based on unsupervised learning model
K Bnou, S Raghay, A Hakim - EURASIP Journal on Advances in Signal …, 2020 - Springer
Image denoising plays an important role in image processing, which aims to separate clean
images from noisy images. A number of methods have been presented to deal with this …
images from noisy images. A number of methods have been presented to deal with this …
Big data analytics on lung cancer diagnosis framework with deep learning
P Guan, K Yu, W Wei, YL Tan… - IEEE/ACM transactions on …, 2023 - ieeexplore.ieee.org
As the segment of diseased tissue in PET images is time-consuming, laborious and low
accuracy, this work proposes an automated framework for PET image screening, denoising …
accuracy, this work proposes an automated framework for PET image screening, denoising …
A review on self-adaptation approaches and techniques in medical image denoising algorithms
Noise is a definite degeneration of medical images that interferes with the diagnostic
process in clinical medicine. Although many denoising algorithms have been developed to …
process in clinical medicine. Although many denoising algorithms have been developed to …
Despeckling of clinical ultrasound images using deep residual learning
P Kokil, S Sudharson - Computer Methods and Programs in Biomedicine, 2020 - Elsevier
Background and objective Ultrasound is the non-radioactive imaging modality used in the
diagnosis of various diseases related to the internal organs of the body. The presence of …
diagnosis of various diseases related to the internal organs of the body. The presence of …
A denoising framework for 3D and 2D imaging techniques based on photon detection statistics
A method to capture three-dimensional (3D) objects image data under extremely low light
level conditions, also known as Photon Counting Imaging (PCI), was reported. It is …
level conditions, also known as Photon Counting Imaging (PCI), was reported. It is …