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Computer Vision and Machine Intelligence in Medical Image Analysis

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Cover of 'Computer Vision and Machine Intelligence in Medical Image Analysis'

Table of Contents

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    Book Overview
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    Chapter 1 A Novel Method for Pneumonia Diagnosis from Chest X-Ray Images Using Deep Residual Learning with Separable Convolutional Networks
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    Chapter 2 Identification of Neural Correlates of Face Recognition Using Machine Learning Approach
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    Chapter 3 An Overview of Remote Photoplethysmography Methods for Vital Sign Monitoring
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    Chapter 4 Fuzzy Inference System for Efficient Lung Cancer Detection
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    Chapter 5 Medical Image Compression Scheme Using Number Theoretic Transform
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    Chapter 6 The Retinal Blood Vessel Segmentation Using Expected Maximization Algorithm
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    Chapter 7 Classification Algorithms to Predict Heart Diseases—A Survey
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    Chapter 8 A Hybrid Filtering-Based Retinal Blood Vessel Segmentation Algorithm
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    Chapter 9 Laser Scar Classification in Retinal Fundus Images Using Wavelet Transform and Local Variance
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    Chapter 10 Automated Segmentation of Cervical Cells Using MSER Algorithm and Gradient Embedded Cost Function-Based Level-Set Method
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    Chapter 11 Macroscopic Reconstruction for Histopathology Images: A Survey
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    Chapter 12 Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques
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    Chapter 13 Medical Diagnosis Under Uncertain Environment Through Bipolar-Valued Fuzzy Sets
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    Chapter 14 Design and Analysis of Novel Room Temperature T-Ray Source for Biomedical Imaging: Application in Full Body Prosthetics
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Title
Computer Vision and Machine Intelligence in Medical Image Analysis
Published by
Springer Singapore, August 2019
DOI 10.1007/978-981-13-8798-2
ISBNs
978-9-81-138797-5, 978-9-81-138798-2
Editors

Gupta, Mousumi, Konar, Debanjan, Bhattacharyya, Siddhartha, Biswas, Sambhunath

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Mendeley readers

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

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 3 300%
Readers by discipline Count As %
Computer Science 2 200%
Engineering 1 100%