O
Ozal Yildirim
Researcher at Tunceli University
Publications - 46
Citations - 5914
Ozal Yildirim 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 21, co-authored 46 publications receiving 3040 citations. Previous affiliations of Ozal Yildirim include Namik Kemal University & Uludağ University.
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.
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Arrhythmia detection using deep convolutional neural network with long duration ECG signals.
TL;DR: A new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis based on a new 1D-Convolutional Neural Network model (1D-CNN).
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A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification
TL;DR: It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks and is an important approach that can be applied to similar signal processing problems.
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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.
Journal ArticleDOI
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.