Welcome to Biomedical Signal and Image Processing Lab.

In this lab, the members of the CTG Research Group carry out the various studies using the novel trend methods. You can find the related papers, source codes, and more in this section. 


Automatic determination of digital modulation types with different noises using Convolutional Neural Network based on time–frequency information
DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images
Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images
A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models
BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer
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
Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks
BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model
Waste Classification using AutoEncoder Network with Integrated Feature Selection Method in Convolutional Neural Network Models
Classification of Brain MRI Using Hyper Column Technique with Convolutional Neural Network and Feature Selection Method
A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods
Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks
Detection of weather images by using spiking neural networks of deep learning models
Intelligent skin cancer detection applying autoencoder, MobileNetV2 and spiking neural networks
Enhancing of dataset using DeepDream, fuzzy color image enhancement and hypercolumn techniques to detection of the Alzheimer's disease stages by deep learning model
Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism
Machine Learning Approach Equipped with Neighbourhood Component Analysis for DDoS Attack Detection in Software-Defined Networking

Assoc. Prof. Zafer CÖMERT
Samsun University, Turkey
Web | Scholar | RG

Manonmaniam Sundaranar University, Department of Computer Science and Engineering, India
Web | Scholar | RG

Prof. Dr. Kemal POLAT
Abant İzzetbaysal University, Turkey
Web | Scholar | RG

Dr. Ümit BUDAK
Bitlis Eren University, Turkey
Web | Scholar | RG

Prof. Dr. Abdülkadir ŞENGÜR
Fırat University, Turkey
Web | Scholar | RG

Firat University, Turkey
Web | Scholar | RG

Research Community


The members prepare universal papers collaboratively.

Join us

Please feel free to join us. This is a sharing platform.


The papers published by the community can be examined easily at here.


Soon, you can download CTG-OAS. We are waiting for completion of the publication process.

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.

The lastest publications!

Let's Get In Touch!

Ready to start your next manuscript with us? That's great! Send us an email and we will get back to you as soon as possible!