Accurate phase retrieval of complex 3D point spread functions with deep residual neural networks.
Sign Up to like & getrecommendations! Published in 2019 at "Applied physics letters"
DOI: 10.1063/1.5125252
Abstract: Phase retrieval, i.e., the reconstruction of phase information from intensity information, is a central problem in many optical systems. Imaging the emission from a point source such as a single molecule is one example. Here,… read more here.
Keywords: phase; point; point spread; phase retrieval ... See more keywords
Protein contact prediction using metagenome sequence data and residual neural networks
Sign Up to like & getrecommendations! Published in 2020 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btz477
Abstract: MOTIVATION Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the… read more here.
Keywords: metagenome sequence; neural networks; sequence data; contact prediction ... See more keywords
Universal Approximation Power of Deep Residual Neural Networks Through the Lens of Control
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Automatic Control"
DOI: 10.1109/tac.2022.3190051
Abstract: In this article, we show that deep residual neural networks have the power of universal approximation by using, in an essential manner, the observation that these networks can be modeled as nonlinear control systems. We… read more here.
Keywords: neural networks; universal approximation; deep residual; control ... See more keywords
Metasurface design with a complex residual neural network.
Sign Up to like & getrecommendations! Published in 2023 at "Applied optics"
DOI: 10.1364/ao.478082
Abstract: In recent years, researchers have made great progress in solving complex electromagnetic field computing problems by using deep learning methods. However, the approaches found in literature were devoted to solving the real-number problem of electromagnetic… read more here.
Keywords: neural network; metasurface design; complex residual; residual neural ... See more keywords
ECG classification using 1-D convolutional deep residual neural network
Sign Up to like & getrecommendations! Published in 2023 at "PLOS ONE"
DOI: 10.1371/journal.pone.0284791
Abstract: An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning… read more here.
Keywords: ecg classification; deep residual; classification; ecg ... See more keywords
Research on the classification of lymphoma pathological images based on deep residual neural network
Sign Up to like & getrecommendations! Published in 2021 at "Technology and Health Care"
DOI: 10.3233/thc-218031
Abstract: BACKGROUND: Malignant lymphoma is a type of tumor that originated from the lymphohematopoietic system, with complex etiology, diverse pathological morphology, and classification. It takes a lot of time and energy for doctors to accurately determine… read more here.
Keywords: neural network; deep residual; residual neural; classification ... See more keywords
High-resolution medical image reconstruction based on residual neural network for diagnosis of cerebral aneurysm
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Cardiovascular Medicine"
DOI: 10.3389/fcvm.2022.1013031
Abstract: Objective Cerebral aneurysms are classified as severe cerebrovascular diseases due to hidden and critical onset, which seriously threaten life and health. An effective strategy to control intracranial aneurysms is the regular diagnosis and timely treatment… read more here.
Keywords: network; high resolution; residual neural; resolution ... See more keywords
A Transfer Residual Neural Network Based on ResNet-50 for Detection of Steel Surface Defects
Sign Up to like & getrecommendations! Published in 2023 at "Applied Sciences"
DOI: 10.3390/app13095260
Abstract: With the increasing popularity of deep learning, enterprises are replacing traditional inefficient and non-robust defect detection methods with intelligent recognition technology. This paper utilizes TL (transfer learning) to enhance the model’s recognition performance by integrating… read more here.
Keywords: transfer residual; neural network; residual neural; detection ... See more keywords
A Hybrid Deep Learning Framework Based on Diffusion Model and Deep Residual Neural Network for Defect Detection in Composite Plates
Sign Up to like & getrecommendations! Published in 2023 at "Applied Sciences"
DOI: 10.3390/app13105843
Abstract: The establishment of a structural health monitoring (SHM) system for the damage and defects of composite structures is of great theoretical and engineering value to ensure their production and operational safety. Advanced machine learning technologies,… read more here.
Keywords: deep residual; residual neural; deep learning; model ... See more keywords