Yazar "Karaman, Onur" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim
    Öğe
    Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images
    (PERGAMON-ELSEVIER SCIENCE LTD, 2021) Alhudhaif, Adi; Polat, Kemal; Karaman, Onur
    X-ray units have become one of the most advantageous candidates for triaging the new Coronavirus disease COVID-19 infected patients thanks to its relatively low radiation dose, ease of access, practical, reduced prices, and quick imaging process. This research intended to develop a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views. Moreover, it is aimed to prevent bias issues due to the database. Transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18, and SqueezeNet. The chest X-ray images were acquired from publicly available databases, and each individual image was carefully selected to prevent any bias problem. A stratified 5fold cross-validation approach was utilized with a ratio of 90% for training and 10% for the testing (unseen folds), in which 20% of training data was used as a validation set to prevent overfitting problems. The binary classification performances of the proposed CNN models were evaluated by the testing data. The activation mapping approach was implemented to improve the causality and visuality of the radiograph. The outcomes demonstrated that the proposed CNN model built on DenseNet-201 architecture outperformed amongst the others with the highest accuracy, precision, recall, and F1-scores of 94.96%, 89.74%, 94.59%, and 92.11%, respectively. The results indicated that the reliable diagnosis of COVID-19 pneumonia from CXIs based on the CNN model opens the door to accelerate triage, save critical time, and prioritize resources besides assisting the radiologists.
  • Küçük Resim
    Öğe
    Development of smart camera systems based on artificial intelligence network for social distance detection to fight against COVID-19
    (Elsevier, 2021) Karaman, Onur; Alhudhaif, Adi; Polat, Kemal
    In this work, an artificial intelligence network-based smart camera system prototype, which tracks social distance using a bird’s-eye perspective, has been developed. ‘‘MobileNet SSD-v3’’, ‘‘Faster-RCNN Inception-v2’’, ‘‘Faster-R-CNN ResNet-50’’ models have been utilized to identify people in video sequences. The final prototype based on the Faster R-CNN model is an integrated embedded system that detects social distance with the camera. The software developed using the ‘‘Nvidia Jetson Nano’’ development kit and Raspberry Pi camera module calculates all necessary actions in itself, detects social distance violations, makes audible and light warnings, and reports the results to the server. It is predicted that the developed smart camera prototype can be integrated into public spaces within the ‘‘sustainable smart cities,’’ the scope that the world is on the verge of a change
  • Küçük Resim
    Öğe
    Robust automated Parkinson disease detection based on voice signals with transfer learning
    (PERGAMON-ELSEVIER SCIENCE LTD, 2021) Karaman, Onur; Çakın, Hakan; Alhudhaif, Adi; Polat, Kemal
    Parkinson's disease (PD) is a progressive-neurodegenerative disorder that affects more than 6 million people around the world. However, conventional techniques for PD detection are often hand-crafted, in which special expertise is needed. In this study, considering the importance of rapid diagnosis of the disease, it was aimed to develop deep convolutional neural networks (CNN) for automated PD identification based on biomarkers-derived voice signals. The developed CNN methods consisted of two main stages, including data pre-processing and fine-tunning-based transfer learning steps. To train and evaluate the performance of the developed model, datasets were collected from the mPower Voice database. SqueezeNet1_1, ResNet101, and DenseNet161 architectures were retrained and evaluated to determine which architecture can classify frequency-time information most accurately. The performance results revealed that the proposed model could successfully identify the PD with an accuracy of 89.75%, sensitivity of 91.50%, and precision of 88.40% for DenseNet-161 architecture identified as the most suitable fine-tuning architecture. The results revealed that the proposed model based on transfer learning with a fine-tuning approach provides an acceptable detection of PD with an accuracy of 89.75%. The outcomes of the study confirmed that by integrating the developed model into smart electronic devices, it will be able to develop alternative pre-diagnosis methods and will assist the physicians for PD detection during the in-clinic assessment. The success of the proposed model would imply an enhancement in the life quality of patients and a cost reduction for the national health system.