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 ] Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism
  2. [ BioMedLab ] Enhancing of dataset using DeepDream, fuzzy color image enhancement and hypercolumn techniques to detection of the Alzheimer's disease stages by deep learning model
  3. [ BioMedLab ] Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks
  4. [ Otitis Media ] Otitis media detection using tympanic membrane images with a novel multi-class machine learning algorithm
  5. [ Otitis Media ] Otitis media diagnosis model for tympanic membrane images processed in two-stage processing blocks
  6. [ Otitis Media ] Determination of Tympanic Membrane Region in the Middle Ear Otoscope Images with Convolutional Neural Network Based YOLO Method
  7. [ BioMedLab ] Detection of weather images by using spiking neural networks of deep learning models
  8. [ BioMedLab ] Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks
  9. [ BioMedLab ] Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods
  10. [ CTG-OAS ] Predicting fetal hypoxia using common spatial pattern and machine learning from cardiotocography signals
  11. [ 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
  12. [ BioMedLab ] A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model
  13. [ BioMedLab ] Classification of Brain MRI Using Hyper Column Technique with Convolutional Neural Network and Feature Selection Method
  14. [ BioMedLab ] Waste Classification using AutoEncoder Network with Integrated Feature Selection Method in Convolutional Neural Network Models
  15. [ BioMedLab ] BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model
  16. [ Otitis Media ] Fusing fine-tuned deep features for recognizing different tympanic membranes
  17. [ BioMedLab ] Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks
  18. [ 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
  19. [ BioMedLab ] BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer
  20. [ BioMedLab ] A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models
  21. [ Otitis Media ] Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks
  22. [ Otitis Media ] Convolutional neural network approach for automatic tympanic membrane detection and classification
  23. [ BioMedLab ] Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images
  24. [ BioMedLab ] DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images
  25. [ BioMedLab ] Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information
  26. [ Haploid Maize Seeds ] Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach
  27. [ Haploid Maize Seeds ] Identification of Haploid Maize Seeds using Gray Level Co-occurrence Matrix and Machine Learning Techniques
  28. [ CTG-OAS ] Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models
  29. [ CTG-OAS ] Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
  30. [ CTG-OAS ] Analysis of Fetal Heart Rate Signal based on Neighborhood-based Variance Compression Method
  31. [ CTG-OAS ] The influences of different window functions and lengths on image-based time-frequency features of fetal heart rate signals
  32. [ CTG-OAS ] Performance evaluation of Empirical Mode Decomposition and Discrete Wavelet Transform for computerized hypoxia detection and prediction
  33. [ CTG-OAS ] A novel software for comprehensive analysis of cardiotocography signals “CTG-OAS”
  34. [ CTG-OAS ] A study of artificial neural network training algorithms for classification of cardiotocography signals
  35. [ CTG-OAS ] Cardiotocography analysis based on segmentation-based fractal texture decomposition and extreme learning machine
  36. [ CTG-OAS ] Using wavelet transform for cardiotocography signals classification
  37. [ CTG-OAS ] Evaluation of Fetal Distress Diagnosis during Delivery Stages based on Linear and Nonlinear Features of Fetal Heart Rate for Neural Network Community
  38. [ CTG-OAS ] A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses
  39. [ CTG-OAS ] Cardiotocography signals with artificial neural network and extreme learning machine
  40. [ CTG-OAS ] Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
  41. [ CTG-OAS ] Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment
  42. [ CTG-OAS ] Open-access software for analysis of fetal heart rate signals
  43. [ CTG-OAS ] Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach
  44. [ CTG-OAS ] Computer-Aided Diagnosis System of Fetal Hypoxia Incorporating Recurrence Plot With Convolutional Neural Network
  45. [ CTG-OAS ] A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces

Research Community

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Publications

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CTG-OAS
Preprocessing
CTG-OAS
Linear and Nonlinear Feature Extraction
CTG-OAS
Morphological Feature Extraction
CTG-OAS
Image-based Time-Frequnecy Analysis
CTG-OAS
Feature Selection
CTG-OAS
Classification & Performance Measurement

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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