CTG-OAS


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CTG-OAS


CTG-OAS is an open-access software for analyzing cardiotocography (CTG) signals. The software is developed via Matlab. The main aim of this software is to ensure a computational platform for research purpose. The significant processes such as preprocessing, feature transform and classification in terms of the automated CTG analysis have been embedded into the software to develop new algorithms. 

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Lastest Publication

  1. [ BioMedLab ] COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
  2. [ BioMedLab ] A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model
  3. [ BioMedLab ] Classification of Brain MRI Using Hyper Column Technique with Convolutional Neural Network and Feature Selection Method
  4. [ BioMedLab ] Waste Classification using AutoEncoder Network with Integrated Feature Selection Method in Convolutional Neural Network Models
  5. [ BioMedLab ] BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model
  6. [ Otitis Media ] Fusing fine-tuned deep features for recognizing different tympanic membranes
  7. [ BioMedLab ] Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks
  8. [ BioMedLab ] Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders
  9. [ BioMedLab ] BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer
  10. [ BioMedLab ] A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models
  11. [ Otitis Media ] Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks
  12. [ Otitis Media ] Convolutional neural network approach for automatic tympanic membrane detection and classification
  13. [ BioMedLab ] Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images
  14. [ BioMedLab ] DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images
  15. [ BioMedLab ] Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information
  16. [ Haploid Maize Seeds ] Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach
  17. [ Haploid Maize Seeds ] Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques
  18. [ CTG-OAS ] Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models
  19. [ CTG-OAS ] Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
  20. [ CTG-OAS ] Analysis of Fetal Heart Rate Signal based on Neighborhood-based Variance Compression Method
  21. [ CTG-OAS ] The influences of different window functions and lengths on image-based time-frequency features of fetal heart rate signals
  22. [ CTG-OAS ] Performance evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for computerized hypoxia detection and prediction
  23. [ CTG-OAS ] A novel software for comprehensive analysis of cardiotocography signals “CTG-OAS”
  24. [ CTG-OAS ] A study of artificial neural network training algorithms for classification of cardiotocography signals
  25. [ CTG-OAS ] Cardiotocography analysis based on segmentation-based fractal texture decomposition and extreme learning machine
  26. [ CTG-OAS ] Using wavelet transform for cardiotocography signals classification
  27. [ CTG-OAS ] Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community
  28. [ CTG-OAS ] A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses
  29. [ CTG-OAS ] Cardiotocography signals with artificial neural network and extreme learning machine
  30. [ CTG-OAS ] Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
  31. [ CTG-OAS ] Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment
  32. [ CTG-OAS ] Open-access software for analysis of fetal heart rate signals
  33. [ CTG-OAS ] Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
  34. [ CTG-OAS ] Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network
  35. [ CTG-OAS ] A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces

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Please feel free to meet and join us.


This is an academic platform. Please feel free to meet and join us. A wide variety of talented members give our team the opportunity to innovate in nearly every domain of Biomedical Signal Processing, especially Cardiotocography.

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