AIMC Topic: Deep Learning

Clear Filters Showing 24691 to 24700 of 28423 articles

Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.

Explainable Deep Learning for Glaucomatous Visual Field Prediction: Artifact Correction Enhances Transformer Models.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop a deep learning approach that restores artifact-laden optical coherence tomography (OCT) scans and predicts functional loss on the 24-2 Humphrey Visual Field (HVF) test.

The Associations Between Myopia and Fundus Tessellation in School Children: A Comparative Analysis of Macular and Peripapillary Regions Using Deep Learning.

Translational vision science & technology
PURPOSE: To evaluate the refractive differences among school-aged children with macular or peripapillary fundus tessellation (FT) distribution patterns, using fundus tessellation density (FTD) quantified by deep learning (DL) technology.

Deep Learning-Based SD-OCT Layer Segmentation Quantifies Outer Retina Changes in Patients With Biallelic RPE65 Mutations Undergoing Gene Therapy.

Investigative ophthalmology & visual science
PURPOSE: To quantify outer retina structural changes and define novel biomarkers of inherited retinal degeneration associated with biallelic mutations in RPE65 (RPE65-IRD) in patients before and after subretinal gene augmentation therapy with voretig...

RRFNet: A free-anchor brain tumor detection and classification network based on reparameterization technology.

PloS one
Advancements in medical imaging technology have facilitated the acquisition of high-quality brain images through computed tomography (CT) or magnetic resonance imaging (MRI), enabling professional brain specialists to diagnose brain tumors more effec...

Comparing UNet configurations for anthropogenic geomorphic feature extraction from land surface parameters.

PloS one
The application of deep learning for semantic segmentation has revolutionized image analysis, particularly in the geospatial and medical fields. UNet, an encoder-decoder architecture, has been suggested to be particularly effective. However, limitati...

Enhancing ECG disease detection accuracy through deep learning models and P-QRS-T waveform features.

PloS one
Cardiovascular diseases (CVDs) have surpassed cancer and become the major cause of death worldwide. An electrocardiogram (ECG) is a non-invasive and quicker method for diagnosing abnormal heart conditions. While research has extensively focused on EC...

Student engagement assessment using multimodal deep learning.

PloS one
Student engagement assessment plays an important role in enhancing students' positive performance and optimizing teaching methods. In this paper, a multimodal deep learning framework is proposed for student engagement assessment. Based on this framew...

The Advances in Deep Learning Modeling of Polyadenylation Codes.

Wiley interdisciplinary reviews. RNA
3'-end cleavage and polyadenylation is an essential step of eukaryotic mRNA and lncRNA expression. The formation of a polyadenylation (polyA) site is determined by combinatory effects of multiple tandem motifs (~6 motifs in humans), each of which is ...

Segmentation-based deep 2D-3D multibranch learning approach for effective hyperspectral image classification.

PloS one
Deep learning has revolutionized the classification of land cover objects in hyperspectral images (HSIs), particularly by managing the complex 3D cube structure inherent in HSI data. Despite these advances, challenges such as data redundancy, computa...