Convolutional Neural Networks for Medical Image Processing Applications

Author:   Saban Ozturk
Publisher:   Taylor & Francis Ltd
ISBN:  

9781032104003


Pages:   268
Publication Date:   23 December 2022
Format:   Hardback
Availability:   In Print   Availability explained
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Convolutional Neural Networks for Medical Image Processing Applications


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Author:   Saban Ozturk
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
Weight:   0.520kg
ISBN:  

9781032104003


ISBN 10:   1032104007
Pages:   268
Publication Date:   23 December 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Convolutional neural networks for segmentation in short-axis cine cardiac magnetic resonance imaging: review and considerations. Comparison of Traditional Machine Learning Algorithms and Convolution Neural Networks for Detection of Peripheral Malarial Parasites in Blood Smears. Deep Learning-Based Computer-Aided Diagnosis System for Attention Deficit Hyperactivity Disorder Classification Using Synthetic Data. Basic Ensembles of Vanilla-Style Deep Learning Models Improve Liver Segmentation from CT Images. Convolutional Neural Networks for Medical Image Analysis. Ulcer and Red Lesion Detection in Wireless Capsule Endoscopy Images using CNN. Do More with Less: Deep Learning in Medical Imaging. Automatic Classification of fMRI Signals from Behavioral, Cognitive and Affective Tasks Using Deep Learning. Detection of COVID-19 in Lung CT-Scans using Reconstructed Image Features. Dental image analysis: Where deep learning meets dentistry. Malarial Parasite Detection in Blood Smear Microscopic Images: A Review on Deep Learning Approaches. Automatic Classification of Coronary Stenos is using Convolutional Neural Networks and Simulated Annealing. Deep Learning Approach for Detecting COVID-19 from Chest X-ray Images.

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Author Information

Şaban Öztürk is an Associate Professor in Amasya University, Amasya, Turkey. He obtained his B.S., M.S. Ph.D. in Electrical and Electronics Engineering from Selçuk University, Turkey in 2011, 2015, and 2019, respectively. He lectures in artificial intelligence and image processing related courses at the Amasya University. Also, he is the head of the Visual Understanding in Biomedical Images laboratory. His research interests encompass artificial intelligence, medical image analysis and deep learning applications. He has more than 50 published articles and proceedings.

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