AIMC Topic: Neural Networks, Computer

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Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy.

Photodiagnosis and photodynamic therapy
With deep convolutional neural networks and fiber optic Raman spectroscopy, this study presents a novel classification method that discriminates tongue squamous cell carcinoma (TSCC) from non-tumorous tissue. To achieve this purpose, 24 tissues spect...

Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans.

Sensors (Basel, Switzerland)
Intracranial hemorrhage is a medical emergency that requires urgent diagnosis and immediate treatment to improve patient outcome. Machine learning algorithms can be used to perform medical image classification and assist clinicians in diagnosing radi...

India: Intruder Node Detection and Isolation Action in Mobile Ad Hoc Networks Using Feature Optimization and Classification Approach.

Journal of medical systems
Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy...

Electrical stimulation in a spiking neural network model of monkey superior colliculus.

Progress in brain research
The superior colliculus (SC) generates saccades by recruiting a population of cells in its topographically organized motor map. Supra-threshold electrical stimulation in the SC produces a normometric saccade with little effect of the stimulation para...

Redundant feature pruning for accelerated inference in deep neural networks.

Neural networks : the official journal of the International Neural Network Society
This paper presents an efficient technique to reduce the inference cost of deep and/or wide convolutional neural network models by pruning redundant features (or filters). Previous studies have shown that over-sized deep neural network models tend to...

Detection of smoking status from retinal images; a Convolutional Neural Network study.

Scientific reports
Cardiovascular diseases are directly linked to smoking habits, which has both physiological and anatomical effects on the systemic and retinal circulations, and these changes can be detected with fundus photographs. Here, we aimed to 1- design a Conv...

Novel stochastic framework for automatic segmentation of human thigh MRI volumes and its applications in spinal cord injured individuals.

PloS one
Severe spinal cord injury (SCI) leads to skeletal muscle atrophy and adipose tissue infiltration in the skeletal muscle, which can result in compromised muscle mechanical output and lead to health-related complications. In this study, we developed a ...

Cognitive Action Laws: The Case of Visual Features.

IEEE transactions on neural networks and learning systems
This paper proposes a theory for understanding perceptual learning processes within the general framework of laws of nature. Artificial neural networks are regarded as systems whose connections are Lagrangian variables, namely, functions depending on...

Classification of cow milk using artificial neural network developed from the spectral data of single- and three-detector spectrophotometers.

Food chemistry
Spectra data from two instruments (UV-Vis/NIR and FT-NIR) consisting of three and one detectors, respectively, were employed in order to discriminate the geographical origin of milk as a way to detect adulteration. Initially, principal component anal...

Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Cancer invasion depth is a critical factor affecting the choice of treatment in patients with superficial squamous cell carcinoma (SCC). However, the diagnosis of invasion depth is currently subjective and liable to interobserver...