AIMC Topic: Neural Networks, Computer

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Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.

Interdisciplinary sciences, computational life sciences
Accurate segregation of retinal blood vessels network plays a crucial role in clinical assessments, treatments, and rehabilitation process. Owing to the presence of acquisition and instrumentation anomalies, precise tracking of vessels network is cha...

Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality.

Medical physics
BACKGROUND: Due to intrinsic differences in data formatting, data structure, and underlying semantic information, the integration of imaging data with clinical data can be non-trivial. Optimal integration requires robust data fusion, that is, the pro...

Acoustic Emission and Artificial Intelligence Procedure for Crack Source Localization.

Sensors (Basel, Switzerland)
The acoustic emission (AE) technique is one of the most widely used in the field of structural monitoring. Its popularity mainly stems from the fact that it belongs to the category of non-destructive techniques (NDT) and allows the passive monitoring...

Fault Identification and Localization of a Time-Frequency Domain Joint Impedance Spectrum of Cables Based on Deep Belief Networks.

Sensors (Basel, Switzerland)
To improve the accuracy of shallow neural networks in processing complex signals and cable fault diagnosis, and to overcome the shortage of manual dependency and cable fault feature extraction, a deep learning method is introduced, and a time-frequen...

Constructing Physics-Informed Neural Networks with Architecture Based on Analytical Modification of Numerical Methods by Solving the Problem of Modelling Processes in a Chemical Reactor.

Sensors (Basel, Switzerland)
A novel type of neural network with an architecture based on physics is proposed. The network structure builds on a body of analytical modifications of classical numerical methods. A feature of the constructed neural networks is defining parameters o...

An Effective Framework for Deep-Learning-Enhanced Quantitative Microwave Imaging and Its Potential for Medical Applications.

Sensors (Basel, Switzerland)
Microwave imaging is emerging as an alternative modality to conventional medical diagnostics technologies. However, its adoption is hindered by the intrinsic difficulties faced in the solution of the underlying inverse scattering problem, namely non-...

Urban Flooding Prediction Method Based on the Combination of LSTM Neural Network and Numerical Model.

International journal of environmental research and public health
At present, urban flood risk analysis and forecasting and early warning mainly use numerical models for simulation and analysis, which are more accurate and can reflect urban flood risk well. However, the calculation speed of numerical models is slow...

On lightmyography based muscle-machine interfaces for the efficient decoding of human gestures and forces.

Scientific reports
Conventional muscle-machine interfaces like Electromyography (EMG), have significant drawbacks, such as crosstalk, a non-linear relationship between the signal and the corresponding motion, and increased signal processing requirements. In this work, ...

A deep network embedded with rough fuzzy discretization for OCT fundus image segmentation.

Scientific reports
The noise and redundant information are the main reasons for the performance bottleneck of medical image segmentation algorithms based on the deep learning. To this end, we propose a deep network embedded with rough fuzzy discretization (RFDDN) for O...

A fully automatic target detection and quantification strategy based on object detection convolutional neural network YOLOv3 for one-step X-ray image grading.

Analytical methods : advancing methods and applications
Methods for automatic image analysis are demanded for dealing with the explosively increased imaging data in clinics. Osteoarthritis (OA) is a typical disease diagnosed based on X-ray imaging. Herein, we propose a novel modeling strategy based on YOL...