U
Ulas Baran Baloglu
Researcher at Tunceli University
Publications - 21
Citations - 3714
Ulas Baran Baloglu is an academic researcher from Tunceli University. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 12, co-authored 19 publications receiving 1785 citations. Previous affiliations of Ulas Baran Baloglu include Fırat University & University of Bristol.
Papers
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Journal ArticleDOI
Automated detection of COVID-19 cases using deep neural networks with X-ray images.
Tülin Öztürk,Muhammed Talo,Eylul Azra Yildirim,Ulas Baran Baloglu,Ozal Yildirim,U. Rajendra Acharya +5 more
TL;DR: A new model for automatic COVID-19 detection using raw chest X-ray images is presented and can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
Journal ArticleDOI
Application of deep transfer learning for automated brain abnormality classification using MR images
Muhammed Talo,Ulas Baran Baloglu,Ozal Yildirim,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: This study proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images, and achieved 5-fold classification accuracy of 100% on 613 MR images.
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Classification of myocardial infarction with multi-lead ECG signals and deep CNN
Ulas Baran Baloglu,Muhammed Talo,Ozal Yildirim,Ru San Tan,U. Rajendra Acharya,U. Rajendra Acharya +5 more
TL;DR: A deep learning model with an end-to-end structure on the standard 12-lead ECG signal for the diagnosis of MI has the potential to provide high performance on MI detection which can be used in wearable technologies and intensive care units.
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A new approach for arrhythmia classification using deep coded features and LSTM networks.
TL;DR: A novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.
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A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals.
TL;DR: A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages and is ready for clinical usage, and can be tested with big PSG data.