AIMC Topic: Deep Learning

Clear Filters Showing 1651 to 1660 of 26499 articles

A hybrid optimization-enhanced 1D-ResCNN framework for epileptic spike detection in scalp EEG signals.

Scientific reports
In order to detect epileptic spikes, this paper suggests a deep learning architecture that blends 1D residual convolutional neural networks (1D-ResCNN) with a hybrid optimization strategy. The Layer-wise Adaptive Moments (LAMB) and AdamW algorithms h...

Multicenter study on predicting postoperative upper limb muscle strength improvement in cervical spinal cord injury patients using radiomics and deep learning.

Scientific reports
Cervical spinal cord injury is often catastrophic, frequently leading to irreversible impairment. MRI has become the gold standard for evaluating spinal cord injuries (SCI). Our study aimed to assess the accuracy of a radiomics approach, based on mac...

Dense convolution-based attention network for Alzheimer's disease classification.

Scientific reports
Recently, deep learning-based medical image classification models have made substantial advancements. However, many existing models prioritize performance at the cost of efficiency, limiting their practicality in clinical use. Traditional Convolution...

Determining human resource management key indicators and their impact on organizational performance using deep reinforcement learning.

Scientific reports
Performance-related indicators are crucial for evaluating and forecasting performance, enhancing decision-making efficiency, and establishing sustainable growth strategies. They motivate individuals and organizations, increase transparency, and accur...

Stacked CNN-based multichannel attention networks for Alzheimer disease detection.

Scientific reports
Alzheimer's Disease (AD) is a progressive condition of a neurological brain disorder recognized by symptoms such as dementia, memory loss, alterations in behaviour, and impaired reasoning abilities. Recently, many researchers have been working to dev...

An 8-point scale lung ultrasound scoring network fusing local detail and global features.

Scientific reports
Manual lung ultrasound (LUS) scoring is influenced by clinicians' subjective interpretation, leading to potential inconsistencies and misdiagnoses due to varying levels of experience. To improve monitoring of pulmonary ventilation and support early d...

Latent alignment in deep learning models for EEG decoding.

Journal of neural engineering
. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencephalography (EEG) signals across individuals. While recent approaches have focused on standardizing input signal distributions, we propose that aligning ...

Multi-label segmentation of carpal bones in MRI using expansion transfer learning.

Physics in medicine and biology
The purpose of this study was to develop a robust deep learning approach trained with a smallMRI dataset for multi-label segmentation of all eight carpal bones for therapy planning and wrist dynamic analysis.A small dataset of 15 3.0-T MRI scans from...

Complex conjugate removal in optical coherence tomography using phase aware generative adversarial network.

Journal of biomedical optics
SIGNIFICANCE: Current methods for complex conjugate removal (CCR) in frequency-domain optical coherence tomography (FD-OCT) often require additional hardware components, which increase system complexity and cost. A software-based solution would provi...

Mixed-Supervised Learning for Cell Classification.

Sensors (Basel, Switzerland)
Cell classification based on histopathology images is crucial for tumor recognition and cancer diagnosis. Using deep learning, classification accuracy is hugely improved. Semi-supervised learning is an advanced deep learning approach that uses both l...