Convolutional Neural Networks for Medical Image Processing Applications (eBook, PDF)

Redaktion: Ozturk, Saban

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 274
• Erscheinungstermin: 23. Dezember 2022
• Englisch
• ISBN-13: 9781000818024
• Artikelnr.: 66360057

Autorenporträt

¿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.

Inhaltsangabe

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.