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[HTML][HTML] Machine learning-based data-driven fault detection/diagnosis of lithium-ion battery: A critical review
Fault detection/diagnosis has become a crucial function of the battery management system
(BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated …
(BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated …
[HTML][HTML] 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 …
[HTML][HTML] Accurate brain tumor detection using deep convolutional neural network
Detection and Classification of a brain tumor is an important step to better understanding its
mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging …
mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging …
Disease prediction by machine learning over big data from healthcare communities
With big data growth in biomedical and healthcare communities, accurate analysis of
medical data benefits early disease detection, patient care, and community services …
medical data benefits early disease detection, patient care, and community services …
Pathological brain detection based on AlexNet and transfer learning
The aim of this study is to automatically detect pathological brain in magnetic resonance
images (MRI) based on deep learning structure and transfer learning. Deep learning is now …
images (MRI) based on deep learning structure and transfer learning. Deep learning is now …
Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU
Multiple sclerosis is a condition affecting brain and/or spinal cord. Based on deep learning,
this study aims to develop an improved convolutional neural network system. We collected …
this study aims to develop an improved convolutional neural network system. We collected …
A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis
Abstract Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more
than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect …
than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect …
Self-supervised multi-modal hybrid fusion network for brain tumor segmentation
Accurate medical image segmentation of brain tumors is necessary for the diagnosing,
monitoring, and treating disease. In recent years, with the gradual emergence of multi …
monitoring, and treating disease. In recent years, with the gradual emergence of multi …
Multiple sclerosis identification by 14-layer convolutional neural network with batch normalization, dropout, and stochastic pooling
SH Wang, C Tang, J Sun, J Yang, C Huang… - Frontiers in …, 2018 - frontiersin.org
Aim: Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide
range of symptoms. Hence, the early diagnosis and treatment is quite important. Method …
range of symptoms. Hence, the early diagnosis and treatment is quite important. Method …
Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling
Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure.
Recently, scholars tend to use computer vision based techniques to detect AUD. We …
Recently, scholars tend to use computer vision based techniques to detect AUD. We …