AIMC Topic: Neural Networks, Computer

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Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks.

Artificial intelligence in medicine
The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. Present methods of determining the health of one's heart include doppler based echocardiogram, MDCT ...

Beamforming and Speckle Reduction Using Neural Networks.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
With traditional beamforming methods, ultrasound B-mode images contain speckle noise caused by the random interference of subresolution scatterers. In this paper, we present a framework for using neural networks to beamform ultrasound channel signals...

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively ...

Deep learning-based super-resolution in coherent imaging systems.

Scientific reports
We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-lim...

Classification of Antibacterial Peptides Using Long Short-Term Memory Recurrent Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Antimicrobial peptides are short amino acid sequences that may be antibacterial, antifungal, and antiviral. Most machine learning methodologies applied to identifying antibacterial peptides have developed feature vectors of identical lengths for each...

CE-Net: Context Encoder Network for 2D Medical Image Segmentation.

IEEE transactions on medical imaging
Medical image segmentation is an important step in medical image analysis. With the rapid development of a convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as optic disc segmentation, ...

Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach.

International journal of computer assisted radiology and surgery
PURPOSE: In radiation therapy, a key step for a successful cancer treatment is image-based treatment planning. One objective of the planning phase is the fast and accurate segmentation of organs at risk and target structures from medical images. Howe...

Effects of Food Contamination on Gastrointestinal Morbidity: Comparison of Different Machine-Learning Methods.

International journal of environmental research and public health
Morbidity prediction can be useful in improving the effectiveness and efficiency of medical services, but accurate morbidity prediction is often difficult because of the complex relationships between diseases and their influencing factors. This study...

Using artificial neural networks to predict future dryland responses to human and climate disturbances.

Scientific reports
Land degradation and sediment remobilisation in dryland environments is considered to be a significant global environmental problem. Given the potential for currently stabilised dune systems to reactivate under climate change and increased anthropoge...

Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network.

Scientific reports
A comprehensive understanding of the stimulus-response properties of individual neurons is necessary to crack the neural code of sensory cortices. However, a barrier to achieving this goal is the difficulty of analysing the nonlinearity of neuronal r...