AIMC Topic: Deep Learning

Clear Filters Showing 24121 to 24130 of 28423 articles

Explainable AI for sharp injury identification using transfer learning with pre-trained deep neural networks.

Forensic science international
OBJECTIVE: To investigate an AI-based method for automatically identifying and classifying sharp injuries using deep learning models, evaluate its effectiveness (e.g., accuracy and explainability), and support forensic injury classification.

A magnetic resonance imaging (MRI)-based deep learning radiomics model predicts recurrence-free survival in lung cancer patients after surgical resection of brain metastases.

Clinical radiology
AIM: To develop and validate a magnetic resonance imaging (MRI)-based deep learning radiomics model (DLRM) to predict recurrence-free survival (RFS) in lung cancer patients after surgical resection of brain metastases (BrMs).

Diagnosis of carpal tunnel syndrome using deep learning with comparative guidance.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: This study aims to develop a deep learning model for a robust diagnosis of Carpal Tunnel Syndrome (CTS) based on comparative classification leveraging the ultrasound images of the thenar and hypothenar muscles.

Identification of therapeutics against PfPK6 protein of Plasmodium falciparum: Structure and Deep Learning approach.

Experimental parasitology
The Plasmodium falciparum Protein Kinase 6 (PfPK6) is a serine/threonine protein kinase categorized under the CMGC group, displaying both cyclin-dependent kinases (CDKs) and mitogen-activated protein kinases (MAPKs) activity. Previous research has in...

Benchmarking HEp-2 cell segmentation methods in indirect immunofluorescence images - standard models to deep learning.

Computers in biology and medicine
Indirect Immunofluorescence (IIF) stained Human Epithelial (HEp-2) cells are considered the gold standard for detecting autoimmune diseases. Accurate cell segmentation, though often viewed as an intermediary step to downstream tasks like classificati...

Faster R-CNN approach for estimating global QRS duration in electrocardiograms with a limited quantity of annotated data.

Computers in biology and medicine
In electrocardiography (ECG), measurement of QRS duration (QRSd) is crucial for diagnosing conditions such as left bundle branch block. To address the limited availability of ECG databases with QRS delineation labels, we present a method to use small...

Deep learning for multiple sclerosis lesion classification and stratification using MRI.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Multiple sclerosis (MS) is a chronic neurological disease characterized by inflammation, demyelination, and neurodegeneration within the central nervous system. Conventional magnetic resonance imaging (MRI) techniques often ...

Deep-ATM DL-LSTM: A novel adaptive thresholding model with dual-layer LSTM architecture for real-time driver drowsiness detection using skin conductance signals.

Computers in biology and medicine
Driver drowsiness detection systems are crucial for road safety. However, existing machine learning models struggle to adjust thresholds for Skin Conductance (SC) adaptively signals due to insufficient feature extraction of tonic and phasic responses...

Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review.

Computers in biology and medicine
The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessments and self-reported measures, often resulting in inconsistent and imprecise evaluations. To address this issue and facilitate early intervention, t...

Visceral Fat Quantified by a Fully Automated Deep-Learning Algorithm and Risk of Incident and Recurrent Diverticulitis.

Diseases of the colon and rectum
BACKGROUND: Obesity is a risk factor for diverticulitis. However, it remains unclear whether visceral fat area, a more precise measurement of abdominal fat, is associated with the risk of diverticulitis.