AIMC Topic: Deep Learning

Clear Filters Showing 17581 to 17590 of 28423 articles

Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study.

Sensors (Basel, Switzerland)
Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obst...

A bagging dynamic deep learning network for diagnosing COVID-19.

Scientific reports
COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagg...

A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study.

EBioMedicine
BACKGROUND: Induction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal ...

A Method for Optimal Detection of Lung Cancer Based on Deep Learning Optimized by Marine Predators Algorithm.

Computational intelligence and neuroscience
Lung cancer is the uncontrolled growth of cells in the lung that are made up of two spongy organs located in the chest. These cells may penetrate outside the lungs in a process called metastasis and spread to tissues and organs in the body. In this p...

A deep learning algorithm for automatic detection and classification of acute intracranial hemorrhages in head CT scans.

NeuroImage. Clinical
Acute Intracranial hemorrhage (ICH) is a life-threatening disease that requires emergency medical attention, which is routinely diagnosed using non-contrast head CT imaging. The diagnostic accuracy of acute ICH on CT varies greatly among radiologists...

Multiparametric mapping in the brain from conventional contrast-weighted images using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a deep-learning-based method to quantify multiple parameters in the brain from conventional contrast-weighted images.

Single cortical neurons as deep artificial neural networks.

Neuron
Utilizing recent advances in machine learning, we introduce a systematic approach to characterize neurons' input/output (I/O) mapping complexity. Deep neural networks (DNNs) were trained to faithfully replicate the I/O function of various biophysical...

A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost.

Sensors (Basel, Switzerland)
Currently, face-swapping deepfake techniques are widely spread, generating a significant number of highly realistic fake videos that threaten the privacy of people and countries. Due to their devastating impacts on the world, distinguishing between r...