LAUSR

About LAUSR
Donate
Contact Us

Follow us:

You are not signed in:

Sign Up!


   Articles with "cnn models" as a keyword



OVASO: Integrated binary CNN models to classify COVID‐19, pneumonia and healthy lung in X‐ray images

Sign Up to like & get
recommendations! Published in 2022 at "International Journal of Imaging Systems and Technology"

DOI: 10.1002/ima.22742

Abstract: Several radiologists have paid attention to computer‐aided detection (CAD) systems which assist in classifying diseases on chest x‐ray (CXR). Recently, with the outbreak of COVID‐19, CAD based on deep learning has an important role in… read more here.

Keywords: integrated binary; ray; models classify; cnn models ... See more keywords
Photo from archive.org

Prediction of activity cliffs on the basis of images using convolutional neural networks

Sign Up to like & get
recommendations! Published in 2021 at "Journal of Computer-Aided Molecular Design"

DOI: 10.1007/s10822-021-00380-y

Abstract: An activity cliff (AC) is formed by a pair of structurally similar compounds with a large difference in potency. Accordingly, ACs reveal structure–activity relationship (SAR) discontinuity and provide SAR information for compound optimization. Herein, we… read more here.

Keywords: acs; convolutional neural; cnn models; prediction activity ... See more keywords

Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis

Sign Up to like & get
recommendations! Published in 2024 at "Neuroinformatics"

DOI: 10.1007/s12021-024-09704-3

Abstract: Meningioma, the most common primary brain tumor, presents significant challenges in MRI-based diagnosis and treatment planning due to its diverse manifestations. Convolutional Neural Networks (CNNs) have shown promise in improving the accuracy and efficiency of… read more here.

Keywords: meningioma segmentation; cnn models; meta analysis; segmentation ... See more keywords
Photo from archive.org

Assisting the Visually Impaired in Multi-object Scene Description Using OWA-Based Fusion of CNN Models

Sign Up to like & get
recommendations! Published in 2020 at "Arabian Journal for Science and Engineering"

DOI: 10.1007/s13369-020-04799-7

Abstract: Advances in technology can provide a lot of support for visually impaired (VI) persons. In particular, computer vision and machine learning can provide solutions for object detection and recognition. In this work, we propose a… read more here.

Keywords: visually impaired; assisting visually; based fusion; cnn models ... See more keywords

Addressing Out‐of‐Sample Issues in Multi‐Layer Convolutional Neural‐Network Parameterization of Mesoscale Eddies Applied Near Coastlines

Sign Up to like & get
recommendations! Published in 2024 at "Journal of Advances in Modeling Earth Systems"

DOI: 10.1029/2024ms004819

Abstract: This study addresses the boundary artifacts in machine‐learned (ML) parameterizations for ocean subgrid mesoscale momentum forcing, as identified in the online ML implementation from a previous study (Zhang et al., 2023, https://doi.org/10.1029/2023ms003697). We focus on… read more here.

Keywords: convolutional neural; cnn models; replicate padding; mesoscale ... See more keywords

Automatic detection of harmful cyanobacterial genera using deep CNN models and artemisinin optimization

Sign Up to like & get
recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-04091-4

Abstract: Concerns over the spread of Cyanobacteria, which can lead to dangerous blooms that harm drinking water quality and, therefore, the health of plants and animals, are being raised by global warming. Traditional methods for assessing… read more here.

Keywords: cnn models; automatic detection; water; detection harmful ... See more keywords

Hybrid neural network method for damage localization in structural health monitoring

Sign Up to like & get
recommendations! Published in 2025 at "Scientific Reports"

DOI: 10.1038/s41598-025-92396-9

Abstract: The detection of cracks in large structures is of critical importance, as such damage can result not only in significant financial costs but also pose serious risks to public safety. Many existing methods for crack… read more here.

Keywords: cnn models; damage; rnn; crack detection ... See more keywords

RS-DeepSuperLearner: fusion of CNN ensemble for remote sensing scene classification

Sign Up to like & get
recommendations! Published in 2023 at "Annals of GIS"

DOI: 10.1080/19475683.2023.2165544

Abstract: ABSTRACT Scene classification is an important problem in remote sensing (RS) and has attracted a lot of research in the past decade. Nowadays, most proposed methods are based on deep convolutional neural network (CNN) models,… read more here.

Keywords: scene classification; cnn; cnn models; deepsuperlearner ... See more keywords

Use of deep learning in forensic sex estimation of virtual pelvic models from the Han population

Sign Up to like & get
recommendations! Published in 2022 at "Forensic Sciences Research"

DOI: 10.1080/20961790.2021.2024369

Abstract: Abstract Accurate sex estimation is crucial to determine the identity of human skeletal remains effectively. Here, we developed convolutional neural network (CNN) models for sex estimation on virtual hemi-pelvic regions, including the ventral pubis (VP),… read more here.

Keywords: estimation virtual; sex; cnn models; deep learning ... See more keywords

Land classification in satellite images by injecting traditional features to CNN models

Sign Up to like & get
recommendations! Published in 2022 at "Remote Sensing Letters"

DOI: 10.1080/2150704x.2023.2167057

Abstract: ABSTRACT Deep learning methods have been successfully applied to remote-sensing problems for several years. Among these methods, CNN-based models have high accuracy in solving the land classification problem using satellite or aerial images. Although these… read more here.

Keywords: accuracy; injecting traditional; cnn models; land classification ... See more keywords

Addressing data scarcity using audio signal augmentation and deep learning for bolt looseness prediction

Sign Up to like & get
recommendations! Published in 2024 at "Smart Materials and Structures"

DOI: 10.1088/1361-665x/ad5c24

Abstract: Deep learning models such as convolutional neural networks (CNNs) encounter challenges, including instability and overfitting, while predicting bolt looseness in data-scarce scenarios. In this study, we proposed a novel audio signal augmentation approach to classify… read more here.

Keywords: cnn models; bolt looseness; looseness; signal augmentation ... See more keywords