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Classification in BioApps

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Cover of 'Classification in BioApps'

Table of Contents

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    Book Overview
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    Chapter 1 Medical Imaging and Its Objective Quality Assessment: An Introduction
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    Chapter 2 A Novel Approach for the Classification of Liver MR Images Using Complex Orthogonal Ripplet-II and Wavelet-Based Transforms
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    Chapter 3 ECG Based Myocardial Infarction Detection Using Different Classification Techniques
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    Chapter 4 Classification and Decision Making of Medical Infrared Thermal Images
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    Chapter 5 Evaluating the Efficacy of Gabor Features in the Discrimination of Breast Density Patterns Using Various Classifiers
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    Chapter 6 Machine Learning-Based State-of-the-Art Methods for the Classification of RNA-Seq Data
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    Chapter 7 Two-Step Verifications for Multi-instance Features Selection: A Machine Learning Approach
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    Chapter 8 Machine Learning Based Plant Leaf Disease Detection and Severity Assessment Techniques: State-of-the-Art
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    Chapter 9 Crop Disease Protection Using Parallel Machine Learning Approaches
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    Chapter 10 Computer Aided Diagnosis in Ophthalmology: Deep Learning Applications
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    Chapter 11 Advanced Computational Intelligence Techniques Based Computer Aided Diagnosis System for Cervical Cancer Detection Using Pap Smear Images
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    Chapter 12 Deep Learning for Medical Image Processing: Overview, Challenges and the Future
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    Chapter 13 On the Fly Segmentation of Intravascular Ultrasound Images Powered by Learning of Backscattering Physics
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    Chapter 14 ECG Signal Dimensionality Reduction-Based Atrial Fibrillation Detection
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    Chapter 15 A Bio-application for Accident Victim Identification Using Biometrics
Attention for Chapter 12: Deep Learning for Medical Image Processing: Overview, Challenges and the Future
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Chapter title
Deep Learning for Medical Image Processing: Overview, Challenges and the Future
Chapter number 12
Book title
Classification in BioApps
Published by
Springer, Cham, January 2018
DOI 10.1007/978-3-319-65981-7_12
Book ISBNs
978-3-31-965980-0, 978-3-31-965981-7
Authors

Muhammad Imran Razzak, Saeeda Naz, Ahmad Zaib, Razzak, Muhammad Imran, Naz, Saeeda, Zaib, Ahmad

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,358 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 1357 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 221 16%
Student > Master 206 15%
Student > Bachelor 120 9%
Researcher 116 9%
Student > Doctoral Student 51 4%
Other 180 13%
Unknown 464 34%
Readers by discipline Count As %
Computer Science 405 30%
Engineering 228 17%
Medicine and Dentistry 58 4%
Biochemistry, Genetics and Molecular Biology 27 2%
Physics and Astronomy 25 2%
Other 116 9%
Unknown 499 37%