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E-raamat: Convolutional Neural Networks for Medical Image Processing Applications

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  • Formaat: 274 pages
  • Ilmumisaeg: 23-Dec-2022
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781000818055
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Convolutional Neural Networks for Medical Image Processing ApplicationsSuurem pilt
  • Formaat: 274 pages
  • Ilmumisaeg: 23-Dec-2022
  • Kirjastus: CRC Press
  • Keel: eng
  • ISBN-13: 9781000818055
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This book contains applications of CNN methods. The content is quite extensive, including the application of different CNN methods to various medical image processing problems. Readers will be able to analyze the effects of CNN methods presented in the book in medical applications.



The rise in living standards increases the expectation of people in almost every field. At the forefront is health. Over the past few centuries, there have been major developments in healthcare. Medical device technology and developments in artificial intelligence (AI) are among the most important ones. The improving technology and our ability to harness the technology effectively by means such as AI have led to unprecedented advances, resulting in early diagnosis of diseases. AI algorithms enable the fast and early evaluation of images from medical devices to maximize the benefits.

While developments in the field of AI were quickly adapted to the field of health, in some cases this contributed to the formation of innovative artificial intelligence algorithms. Today, the most effective artificial intelligence method is accepted as deep learning. Convolutional neural network (CNN) architectures are deep learning algorithms used for image processing. This book contains applications of CNN methods. The content is quite extensive, including the application of different CNN methods to various medical image processing problems. Readers will be able to analyze the effects of CNN methods presented in the book in medical applications.

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.

aban 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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