Sparse Feature Aware Noise Removal Technique for Brain Multiple Sclerosis Lesions using Magnetic Resonance Imaging
Medical Resonance Imaging (MRI) is non-radioactive-based medical imaging that provides a super-resolution of tissues. However, because of its complex nature using existing Deep Learning-based noise removal (i.e., Denoising) techniques, the reconstruction quality is poor and time-consuming. An extens...
Main Authors: | Aditya, C.R (Author), Swetha, M.D (Author) |
---|---|
Format: | Article |
Language: | English |
Published: |
Science and Information Organization
2022
|
Subjects: | |
Online Access: | View Fulltext in Publisher |
Similar Items
-
Using Convolutional Encoder Networks to Determine the Optimal Magnetic Resonance Image for the Automatic Segmentation of Multiple Sclerosis
by: Shaurnav Ghosh, et al.
Published: (2021-09-01) -
Investigation of Deep-Learning-Driven Identification of Multiple Sclerosis Patients Based on Susceptibility-Weighted Images Using Relevance Analysis
by: Alina Lopatina, et al.
Published: (2020-12-01) -
Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation
by: Jian Wang, et al.
Published: (2021-09-01) -
Boosting Magnetic Resonance Image Denoising With Generative Adversarial Networks
by: Miao Tian, et al.
Published: (2021-01-01) -
Deep Learning-Based Method to Differentiate Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis
by: Hyunjin Kim, et al.
Published: (2020-11-01)