Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor
problems for people with a detrimental effect on the functioning of the nervous system. In …
problems for people with a detrimental effect on the functioning of the nervous system. In …
Biological network analysis with deep learning
Recent advancements in experimental high-throughput technologies have expanded the
availability and quantity of molecular data in biology. Given the importance of interactions in …
availability and quantity of molecular data in biology. Given the importance of interactions in …
Artificial intelligence for brain diseases: A systematic review
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for
analyzing complex medical data and extracting meaningful relationships in datasets, for …
analyzing complex medical data and extracting meaningful relationships in datasets, for …
Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging
Background Deep learning using convolutional neural networks (CNNs) has shown great
promise in advancing neuroscience research. However, the ability to interpret the CNNs …
promise in advancing neuroscience research. However, the ability to interpret the CNNs …
Medical image identification methods: A review
J Li, P Jiang, Q An, GG Wang, HF Kong - Computers in Biology and …, 2024 - Elsevier
The identification of medical images is an essential task in computer-aided diagnosis,
medical image retrieval and mining. Medical image data mainly include electronic health …
medical image retrieval and mining. Medical image data mainly include electronic health …
Multiple sclerosis diagnosis using machine learning and deep learning: challenges and opportunities
Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which
can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated …
can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated …
An accurate multiple sclerosis detection model based on exemplar multiple parameters local phase quantization: ExMPLPQ
Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the
white matter of the central nervous system that can be detected using magnetic resonance …
white matter of the central nervous system that can be detected using magnetic resonance …
[HTML][HTML] Role of artificial intelligence in MS clinical practice
R Bonacchi, M Filippi, MA Rocca - NeuroImage: Clinical, 2022 - Elsevier
Abstract Machine learning (ML) and its subset, deep learning (DL), are branches of artificial
intelligence (AI) showing promising findings in the medical field, especially when applied to …
intelligence (AI) showing promising findings in the medical field, especially when applied to …
Transfer-transfer model with MSNet: An automated accurate multiple sclerosis and myelitis detection system
Purpose Multiple sclerosis (MS) is a commonly seen neurodegenerative disorder, and early
diagnosis of MS is a crucial issue to promote patient health. Since MS diagnosis is a …
diagnosis of MS is a crucial issue to promote patient health. Since MS diagnosis is a …
Efficacy of transcranial direct current stimulation (tDCS) on balance and gait in multiple sclerosis patients: A machine learning approach
N Marotta, A de Sire, C Marinaro, L Moggio… - Journal of Clinical …, 2022 - mdpi.com
Transcranial direct current stimulation (tDCS) has emerged as an appealing rehabilitative
approach to improve brain function, with promising data on gait and balance in people with …
approach to improve brain function, with promising data on gait and balance in people with …