AIMC Topic: Neural Networks, Computer

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Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.

Journal of medical engineering & technology
Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection ...

Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks.

IEEE journal of biomedical and health informatics
Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of...

Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation.

Journal of biomedical informatics
Word sense disambiguation helps identifying the proper sense of ambiguous words in text. With large terminologies such as the UMLS Metathesaurus ambiguities appear and highly effective disambiguation methods are required. Supervised learning algorith...

Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks.

Environmental monitoring and assessment
Water quality monitoring is a complex issue that requires support tools in order to provide information for water resource management. Budget constraints as well as an inadequate water quality network design call for the development of evaluation too...

Principles for models of neural information processing.

NeuroImage
The goal of cognitive neuroscience is to understand how mental operations are performed by the brain. Given the complexity of the brain, this is a challenging endeavor that requires the development of formal models. Here, I provide a perspective on m...

Blind Nonnegative Source Separation Using Biological Neural Networks.

Neural computation
Blind source separation-the extraction of independent sources from a mixture-is an important problem for both artificial and natural signal processing. Here, we address a special case of this problem when sources (but not the mixing matrix) are known...

Pre-trained convolutional neural networks as feature extractors for tuberculosis detection.

Computers in biology and medicine
It is estimated that in 2015, approximately 1.8 million people infected by tuberculosis died, most of them in developing countries. Many of those deaths could have been prevented if the disease had been detected at an earlier stage, but the most adva...

Memory States and Transitions between Them in Attractor Neural Networks.

Neural computation
Human memory is capable of retrieving similar memories to a just retrieved one. This associative ability is at the base of our everyday processing of information. Current models of memory have not been able to underpin the mechanism that the brain co...

An Evaluation of Artificial Neural Networks in Predicting Pancreatic Cancer Survival.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
OBJECTIVE: This study aims to evaluate the development of an artificial neural network (ANN) method for predicting the survival likelihood of pancreatic adenocarcinoma patients. The ANN predictive model should produce results with a 90% sensitivity.

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

BMC medical informatics and decision making
BACKGROUND: Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is...