AIMC Topic: Neural Networks, Computer

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A machine learning approach to select features important to stroke prognosis.

Computational biology and chemistry
Ischemic stroke is a common neurological disorder, and is still the principal cause of serious long-term disability in the world. Selection of features related to stroke prognosis is highly valuable for effective intervention and treatment. In this s...

Heart sound classification based on improved MFCC features and convolutional recurrent neural networks.

Neural networks : the official journal of the International Neural Network Society
Heart sound classification plays a vital role in the early detection of cardiovascular disorders, especially for small primary health care clinics. Despite that much progress has been made for heart sound classification in recent years, most of them ...

A machine learning-based framework for Predicting Treatment Failure in tuberculosis: A case study of six countries.

Tuberculosis (Edinburgh, Scotland)
Tuberculosis is ranked as the 2nd deadliest disease in the world and is responsible for ten million deaths in 2017. Treatment failure is one of a main reason behind these deaths. Reasons of treatment failure are still unknown and the death rate due t...

Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach.

Computers in biology and medicine
In the clinical diagnosis of cardiovascular diseases, left ventricle (LV) segmentation in cardiac magnetic resonance images (MRI) is an indispensable procedure for doctors. To reduce the time needed for diagnosis, we develop an automatic LV segmentat...

Automatic Cow Location Tracking System using Ear Tag Visual Analysis.

Sensors (Basel, Switzerland)
Nowadays, for numerous reasons, smart farming systems focus on the use of image processing technologies and 5G communications. In this paper, we propose a tracking system for individual cows using an ear tag visual analysis. By using ear tags, the fa...

A fully automated deep learning pipeline for high-throughput colony segmentation and classification.

Biology open
Adenine auxotrophy is a commonly used non-selective genetic marker in yeast research. It allows investigators to easily visualize and quantify various genetic and epigenetic events by simply reading out colony color. However, manual counting of large...

Generative adversarial network based regularized image reconstruction for PET.

Physics in medicine and biology
Positron emission tomography (PET) is an ill-posed inverse problem and suffers high noise due to limited number of detected events. Prior information can be used to improve the quality of reconstructed PET images. Deep neural networks have also been ...

DEEPSMP: A deep learning model for predicting the ectodomain shedding events of membrane proteins.

Journal of bioinformatics and computational biology
Membrane proteins play essential roles in modern medicine. In recent studies, some membrane proteins involved in ectodomain shedding events have been reported as the potential drug targets and biomarkers of some serious diseases. However, there are f...

Hybrid Low-Order and Higher-Order Graph Convolutional Networks.

Computational intelligence and neuroscience
With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of param...