AIMC Journal:
Computational and mathematical methods in medicine

Showing 301 to 310 of 406 articles

TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine.

Computational and mathematical methods in medicine Aug 7, 2020
Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue...
Image Interpretation, Computer-Assisted Deep Learning Mathematical Concepts Computational Biology Tongue Diseases Medicine, Chinese Traditional Humans Tongue Neural Networks, Computer Databases, Factual
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An Automatic Epilepsy Detection Method Based on Improved Inductive Transfer Learning.

Computational and mathematical methods in medicine Aug 3, 2020
Epilepsy is a chronic disease caused by sudden abnormal discharge of brain neurons, causing transient brain dysfunction. The seizures of epilepsy have the characteristics of being sudden and repetitive, which has seriously endangered patients' health...
Epilepsy Seizures Signal Processing, Computer-Assisted Nonlinear Dynamics Regression Analysis Humans Mathematical Concepts Least-Squares Analysis Machine Learning Discriminant Analysis Algorithms Diagnosis, Computer-Assisted Computational Biology
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Research and Verification of Convolutional Neural Network Lightweight in BCI.

Computational and mathematical methods in medicine Aug 1, 2020
With the increasing of depth and complexity of the convolutional neural network, parameter dimensionality and volume of computing have greatly restricted its applications. Based on the SqueezeNet network structure, this study introduces a block convo...
Databases, Factual Deep Learning Models, Neurological Electroencephalography Humans Stochastic Processes Neural Networks, Computer Data Compression Algorithms Signal Processing, Computer-Assisted Facial Expression Brain-Computer Interfaces
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A Comparative Analysis of Visual Encoding Models Based on Classification and Segmentation Task-Driven CNNs.

Computational and mathematical methods in medicine Aug 1, 2020
Nowadays, visual encoding models use convolution neural networks (CNNs) with outstanding performance in computer vision to simulate the process of human information processing. However, the prediction performances of encoding models will have differe...
Visual Perception Artificial Intelligence Humans Visual Cortex Algorithms Neural Networks, Computer Image Processing, Computer-Assisted Databases, Factual Magnetic Resonance Imaging Functional Neuroimaging Models, Neurological Photic Stimulation
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An Epilepsy Detection Method Using Multiview Clustering Algorithm and Deep Features.

Computational and mathematical methods in medicine Aug 1, 2020
The automatic detection of epilepsy is essentially the classification of EEG signals of seizures and nonseizures, and its purpose is to distinguish the different characteristics of seizure brain electrical signals and normal brain electrical signals....
Deep Learning Cluster Analysis Unsupervised Machine Learning Neural Networks, Computer Electroencephalography Epilepsy Humans Algorithms Seizures Brain Signal Processing, Computer-Assisted Diagnosis, Computer-Assisted
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A Simple Method to Train the AI Diagnosis Model of Pulmonary Nodules.

Computational and mathematical methods in medicine Aug 1, 2020
BACKGROUND: The differential diagnosis of subcentimetre lung nodules with a diameter of less than 1 cm has always been one of the problems of imaging doctors and thoracic surgeons. We plan to create a deep learning model for the diagnosis of pulmonar...
Solitary Pulmonary Nodule Diagnosis, Differential Radiographic Image Interpretation, Computer-Assisted Expert Systems Expert Testimony Adenocarcinoma of Lung Deep Learning Lung Neoplasms Carcinoma, Non-Small-Cell Lung Multiple Pulmonary Nodules Artificial Intelligence Databases, Factual Tomography, X-Ray Computed Humans
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Research of Epidemic Big Data Based on Improved Deep Convolutional Neural Network.

Computational and mathematical methods in medicine Jul 22, 2020
In recent years, with the acceleration of the aging process and the aggravation of life pressure, the proportion of chronic epidemics has gradually increased. A large amount of medical data will be generated during the hospitalization of diabetics. I...
Diabetes Mellitus Patient Readmission Epidemics Algorithms Deep Learning Big Data Humans Neural Networks, Computer
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Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network.

Computational and mathematical methods in medicine Jul 20, 2020
EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition algorithm usually includes two steps, namely, feature extraction and feature classification. In feat...
Humans Models, Neurological Electroencephalography Pattern Recognition, Automated Brain-Computer Interfaces Neural Networks, Computer Algorithms Databases, Factual Deep Learning Motor Cortex Mathematical Concepts Imagination
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An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.

Computational and mathematical methods in medicine Jul 14, 2020
Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In t...
Image Interpretation, Computer-Assisted Neuroimaging Mathematical Concepts Computer Simulation Brain Neoplasms Deep Learning Computational Biology Glioma Support Vector Machine Magnetic Resonance Imaging Humans Models, Neurological Neural Networks, Computer Algorithms Databases, Factual
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Electrical Impedance Tomography-Based Abdominal Subcutaneous Fat Estimation Method Using Deep Learning.

Computational and mathematical methods in medicine Jun 11, 2020
This paper proposes a deep learning method based on electrical impedance tomography (EIT) to estimate the thickness of abdominal subcutaneous fat. EIT for evaluating the thickness of abdominal subcutaneous fat is an absolute imaging problem that aims...
Humans Electric Impedance Neural Networks, Computer Tomography, X-Ray Computed Phantoms, Imaging Algorithms Deep Learning Tomography Computer Simulation Computational Biology Subcutaneous Fat, Abdominal Image Interpretation, Computer-Assisted
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