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

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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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