AIMC Topic: Neural Networks, Computer

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Introducing Swish and Parallelized Blind Removal Improves the Performance of a Convolutional Neural Network in Denoising MR Images.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: To improve the performance of a denoising convolutional neural network (DnCNN) and to make it applicable to images with inhomogeneous noise, a refinement involving an activation function (AF) and an application of the refined method for inho...

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.

Genomics, proteomics & bioinformatics
The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported e...

Comparative study using inverse ontology cogency and alternatives for concept recognition in the annotated National Library of Medicine database.

Neural networks : the official journal of the International Neural Network Society
This paper introduces inverse ontology cogency, a concept recognition process and distance function that is biologically-inspired and competitive with alternative methods. The paper introduces inverse ontology cogency as a new alternative method. It ...

Reliability modelling of resting-state functional connectivity.

NeuroImage
Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliabili...

Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging.

Journal of neuroscience methods
BACKGROUND: Deep learning using convolutional neural networks (CNNs) has shown great promise in advancing neuroscience research. However, the ability to interpret the CNNs lags far behind, confounding their clinical translation.

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemb...

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer's Disease Early Discovery.

Sensors (Basel, Switzerland)
Alzheimer's disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning for those potentially affected. This paper uses a three-dimensional DenseNet architecture to detect ...

Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation.

Sensors (Basel, Switzerland)
Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya f...

Convolutional Neural Network-Based Artificial Intelligence for Classification of Protein Localization Patterns.

Biomolecules
Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful metho...

A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments.

Sensors (Basel, Switzerland)
Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natur...