AIMC Topic: Neural Networks, Computer

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Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks.

Brain and cognition
The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis...

Land surface temperature estimating in urbanized landscapes using artificial neural networks.

Environmental monitoring and assessment
Scenario-based land surface temperature (LST) modeling is a powerful tool for adopting proper urban land use planning policies. In this study, using greater Isfahan as a case study, the artificial neural network (ANN) algorithm was utilized to explor...

Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling.

Journal of medical systems
Alzheimer's disease (AD) is a progressive brain disease. The goal of this study is to provide a new computer-vision based technique to detect it in an efficient way. The brain-imaging data of 98 AD patients and 98 healthy controls was collected using...

SPIN2: Predicting sequence profiles from protein structures using deep neural networks.

Proteins
Designing protein sequences that can fold into a given structure is a well-known inverse protein-folding problem. One important characteristic to attain for a protein design program is the ability to recover wild-type sequences given their native bac...

A deep 3D residual CNN for false-positive reduction in pulmonary nodule detection.

Medical physics
PURPOSE: The automatic detection of pulmonary nodules using CT scans improves the efficiency of lung cancer diagnosis, and false-positive reduction plays a significant role in the detection. In this paper, we focus on the false-positive reduction tas...

Pulling force prediction using neural networks.

International journal of occupational safety and ergonomics : JOSE
PURPOSE: In ergonomics and human factors investigations, pulling force (PF) estimation has usually been achieved using various types of biomechanical models, and independent approximation of PF was done with the help of upper extremity joints. Recent...

Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images.

Medical physics
PURPOSE: Accurate 3D image segmentation is a crucial step in radiation therapy planning of head and neck tumors. These segmentation results are currently obtained by manual outlining of tissues, which is a tedious and time-consuming procedure. Automa...

Deep Attention-Based Spatially Recursive Networks for Fine-Grained Visual Recognition.

IEEE transactions on cybernetics
Fine-grained visual recognition is an important problem in pattern recognition applications. However, it is a challenging task due to the subtle interclass difference and large intraclass variation. Recent visual attention models are able to automati...

Boundedness and global robust stability analysis of delayed complex-valued neural networks with interval parameter uncertainties.

Neural networks : the official journal of the International Neural Network Society
In this paper, the boundedness and robust stability for a class of delayed complex-valued neural networks with interval parameter uncertainties are investigated. By using Homomorphic mapping theorem, Lyapunov method and inequality techniques, suffici...

A novel feature extraction technique for pulmonary sound analysis based on EMD.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The stethoscope based auscultation technique is a primary diagnostic tool for chest sound analysis. However, the performance of this method is limited due to its dependency on physicians experience, knowledge and also clarit...