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 …
A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases
IS Stafford, M Kellermann, E Mossotto, RM Beattie… - NPJ digital …, 2020 - nature.com
Autoimmune diseases are chronic, multifactorial conditions. Through machine learning (ML),
a branch of the wider field of artificial intelligence, it is possible to extract patterns within …
a branch of the wider field of artificial intelligence, it is possible to extract patterns within …
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 …
Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes
Background Attention deficit hyperactivity disorder (ADHD) is a ubiquitous
neurodevelopmental disorder affecting many children. Therefore, automated diagnosis of …
neurodevelopmental disorder affecting many children. Therefore, automated diagnosis of …
Ensemble feature selection for stable biomarker identification and cancer classification from microarray expression data
A Wang, H Liu, J Yang, G Chen - Computers in biology and medicine, 2022 - Elsevier
Microarray technology facilitates the simultaneous measurement of expression of tens of
thousands of genes and enables us to study cancers and tumors at the molecular level …
thousands of genes and enables us to study cancers and tumors at the molecular level …
An empirical evaluation of extreme learning machine uncertainty quantification for automated breast cancer detection
Early detection and diagnosis are the key factors in decreasing the breast cancer mortality
rate in medical image analysis. A randomized learning technique called extreme learning …
rate in medical image analysis. A randomized learning technique called extreme learning …
Selection of features for patient-independent detection of seizure events using scalp EEG signals
Epilepsy involves brain abnormalities that may cause sudden seizures or other
uncontrollable body activities. Epilepsy may have substantial impacts on the patient's quality …
uncontrollable body activities. Epilepsy may have substantial impacts on the patient's quality …
A radiomics-based machine learning model for prediction of tumor mutational burden in lower-grade gliomas
Simple Summary Lower-grade glioma (LGG) is a kind of center nervous system neoplasm
that arises from the glial cells. Lower-grade glioma patients have a median survival time in …
that arises from the glial cells. Lower-grade glioma patients have a median survival time in …
Fault diagnosis of marine electric thruster bearing based on fusing multi-sensor deep learning models
X Zhang, C Sheng, W Ouyang, L Zheng - Measurement, 2023 - Elsevier
The data-driven fault diagnosis of bearings is important in the marine electric thruster. For
avoiding information loss when manually extracting features and unreliable diagnosis by a …
avoiding information loss when manually extracting features and unreliable diagnosis by a …
EEG signal classification based on improved variational mode decomposition and deep forest
X Qin, D Xu, X Dong, X Cui, S Zhang - Biomedical Signal Processing and …, 2023 - Elsevier
The study of EEG signals is of great significance for the diagnosis and prevention of brain
disease. Most of the previous studies are based on the binary classification of nonictal and …
disease. Most of the previous studies are based on the binary classification of nonictal and …