AIMC Topic: Neural Networks, Computer

Clear Filters Showing 22741 to 22750 of 31376 articles

Spotting malignancies from gastric endoscopic images using deep learning.

Surgical endoscopy
BACKGROUND: Gastric cancer is a common kind of malignancies, with yearly occurrences exceeding one million worldwide in 2017. Typically, ulcerous and cancerous tissues develop abnormal morphologies through courses of progression. Endoscopy is a routi...

A structure-time parallel implementation of spike-based deep learning.

Neural networks : the official journal of the International Neural Network Society
Motivated by the recent progress of deep spiking neural networks (SNNs), we propose a structure-time parallel strategy based on layered structure and one-time computation over a time window to speed up the prominent spike-based deep learning algorith...

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.

Medical physics
PURPOSE: Accurate segmentation of the breast is required for breast density estimation and the assessment of background parenchymal enhancement, both of which have been shown to be related to breast cancer risk. The MRI breast segmentation task is ch...

Exploring Spatial Influence of Remotely Sensed PM2.5 Concentration Using a Developed Deep Convolutional Neural Network Model.

International journal of environmental research and public health
Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for e...

Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss.

Journal of healthcare engineering
Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning method to improve classification accuracy of pulm...

Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI-NET).

Magnetic resonance in medicine
PURPOSE: Delayed enhancement imaging is an essential component of cardiac MRI, which is used widely for the evaluation of myocardial scar and viability. The selection of an optimal inversion time (TI) or null point (TI ) to suppress the background my...

Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model.

Computational intelligence and neuroscience
Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguis...

Using a Support Vector Machine Based Decision Stage to Improve the Fault Diagnosis on Gearboxes.

Computational intelligence and neuroscience
Gearboxes are mechanical devices that play an essential role in several applications, e.g., the transmission of automotive vehicles. Their malfunctioning may result in economic losses and accidents, among others. The rise of powerful graphical proces...

Adaptive Neural Control of a Class of Stochastic Nonlinear Uncertain Systems With Guaranteed Transient Performance.

IEEE transactions on cybernetics
In this paper, an adaptive neural network control for stochastic nonlinear systems with uncertain disturbances is proposed. The neural network is considered to approximate an uncertain function in a nonlinear system. And computational burden in opera...