M
Muhammed Talo
Researcher at Fırat University
Publications - 28
Citations - 3660
Muhammed Talo is an academic researcher from Fırat University. The author has contributed to research in topics: Deep learning & Shared memory. The author has an hindex of 13, co-authored 24 publications receiving 1688 citations. Previous affiliations of Muhammed Talo include Tunceli 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.
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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Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images
Yusuf Celik,Muhammed Talo,Ozal Yildirim,Murat Karabatak,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +6 more
TL;DR: In this paper, the authors used deep learning pre-trained models, ResNet-50 and DenseNet-161, for the IDC detection task and obtained promising results in classifying magnification independent histopathology images into benign and malignant classes.
Journal ArticleDOI
Convolutional neural networks for multi-class brain disease detection using MRI images.
TL;DR: This model is ready to be tested with huge MRI images of brain abnormalities and the outcome of the model will also help the clinicians to validate their findings after manual reading of the MRI images.