AIMC Topic: Neural Networks, Computer

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Surrogate-Assisted Retinal OCT Image Classification Based on Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
Optical Coherence Tomography (OCT) is beco-ming one of the most important modalities for the noninvasive assessment of retinal eye diseases. As the number of acquired OCT volumes increases, automating the OCT image analysis is becoming increasingly r...

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models.

Computational intelligence and neuroscience
Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly suscept...

Mixed Th2 and non-Th2 inflammatory pattern in the asthma-COPD overlap: a network approach.

International journal of chronic obstructive pulmonary disease
INTRODUCTION: The asthma-chronic obstructive pulmonary disease (COPD) overlap (ACO) is a clinical condition that combines features of those two diseases, and that is difficult to define due to the lack of understanding of the underlying mechanisms. D...

A Pruning Neural Network Model in Credit Classification Analysis.

Computational intelligence and neuroscience
Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs) have been widely accepted as the convincing methods in the...

Neural electrical activity and neural network growth.

Neural networks : the official journal of the International Neural Network Society
The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary...

A review of machine learning in obesity.

Obesity reviews : an official journal of the International Association for the Study of Obesity
Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning prov...

Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network.

Computers in biology and medicine
Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter <1 cm). As part of these treatments, effective detection ...

Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage.

Computers in biology and medicine
Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated to be effective for visualization of the human cartilage matrix at micrometer resolution, thereby capturing osteoarthritis induced changes to chondrocyte organization. This study...

3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation.

IEEE transactions on cybernetics
Accurate segmentation of infant brain images into different regions of interest is one of the most important fundamental steps in studying early brain development. In the isointense phase (approximately 6-8 months of age), white matter and gray matte...

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association
BACKGROUND: Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, ...