AIMC Topic: Neural Networks, Computer

Clear Filters Showing 10541 to 10550 of 31376 articles

Automatic Recognition of Road Damage Based on Lightweight Attentional Convolutional Neural Network.

Sensors (Basel, Switzerland)
An efficient road damage detection system can reduce the risk of road defects to motorists and road maintenance costs to traffic management authorities, for which a lightweight end-to-end road damage detection network is proposed in this paper, aimin...

Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning.

Sensors (Basel, Switzerland)
In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is ...

Guided Depth Completion with Instance Segmentation Fusion in Autonomous Driving Applications.

Sensors (Basel, Switzerland)
Pixel-level depth information is crucial to many applications, such as autonomous driving, robotics navigation, 3D scene reconstruction, and augmented reality. However, depth information, which is usually acquired by sensors such as LiDAR, is sparse....

A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network.

Sensors (Basel, Switzerland)
The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are found helpful in diagnosing cardiac diseases. However, traditional ways, i.e., a medical consultation required effort, knowledge, and time to interpret ...

Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks.

Nature communications
Retrieving the pupil phase of a beam path is a central problem for optical systems across scales, from telescopes, where the phase information allows for aberration correction, to the imaging of near-transparent biological samples in phase contrast m...

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BMC medical informatics and decision making
BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled dat...

Convolutional neural network -based phantom image scoring for mammography quality control.

BMC medical imaging
BACKGROUND: Visual evaluation of phantom images is an important, but time-consuming part of mammography quality control (QC). Consistent scoring of phantom images over the device's lifetime is highly desirable. Recently, convolutional neural networks...

A Deep Learning Model for Automatic Detection and Classification of Disc Herniation in Magnetic Resonance Images.

IEEE journal of biomedical and health informatics
Localization of lumbar discs in magnetic resonance imaging (MRI) is a challenging task, due to a vast range of shape, size, number, and appearance of discs and vertebrae. Based on a review of the cutting-edge methods, the majority of applied techniqu...

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray.

IEEE journal of biomedical and health informatics
Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learning has a great potential in detecting people who are suspected to be infected with COVID-19. However,...

Hybrid Intelligence-Driven Medical Image Recognition for Remote Patient Diagnosis in Internet of Medical Things.

IEEE journal of biomedical and health informatics
In ear of smart cities, intelligent medical image recognition technique has become a promising way to solve remote patient diagnosis in IoMT. Although deep learning-based recognition approaches have received great development during the past decade, ...