book

Deep Learning for Chest Radiographs

Name: Deep Learning for Chest Radiographs
ISBN: 9780323906869

by Yashvi Chandola, Jitendra Virmani, H.S Bhadauria, Papendra Kumar

July 2021

Intermediate to advanced

228 pages

7h 4m

English

Academic Press

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Cover image
Title page
Table of Contents
Copyright
Preface
Acknowledgments
Chapter 1: Introduction
Abstract1.1: Motivation1.2: Introduction to deep learning1.3: Why deep learning in medical image analysis?1.4: Medical imaging1.5: Description of normal and pneumonia chest radiographs1.6: Objective of the book1.7: Book chapter outline
Chapter 2: Review of related work
Abstract2.1: Introduction2.2: Overview of the studies based on the classification of chest radiographs2.3: Concluding remarks
Chapter 3: Methodology adopted for designing of computer-aided classification systems for chest radiographs
Abstract3.1: Introduction3.2: What is a CAC system?3.3: Need for CAC systems3.4: Need for CAC systems for chest radiographs3.5: Types of classifier designs for CAC systems3.6: Deep learning-based CAC system design3.7: Workflow adopted in the present work3.8: Implementation details3.9: Dataset: Kaggle chest X-ray dataset3.10: Dataset description3.11: Dataset generation3.12: Concluding remarks
Chapter 4: End-to-end pre-trained CNN-based computer-aided classification system design for chest radiographs
Abstract4.1: Introduction4.2: Experimental workflow4.3: Transfer learning-based convolutional neural network design4.4: Architecture of end-to-end pre-trained CNNs used in the present work4.5: Decision fusion4.6: Experiments and results4.7: Concluding remarks

Chapter 5: Hybrid computer-aided classification system design using end-to-end CNN-based deep feature extraction and ANFC-LH classifier for chest radiographs
Abstract5.1: Introduction5.2: Experimental workflow5.3: Deep feature extraction5.4: Feature selection5.5: Adaptive neuro-fuzzy classifier5.6: Experiment and result5.7: Concluding remarks
Chapter 6: Hybrid computer-aided classification system design using end-to-end Pre-trained CNN-based deep feature extraction and PCA-SVM classifier for chest radiographs
Abstract6.1: Introduction6.2: Experimental workflow6.3: Deep feature extraction6.4: Feature selection and dimensionality reduction6.5: SVM classifier6.6: Experiment and result6.7: Concluding remarks
Chapter 7: Lightweight end-to-end Pre-trained CNN-based computer-aided classification system design for chest radiographs
Abstract7.1: Introduction7.2: Experimental workflow7.3: Lightweight CNN model7.4: Architecture of lightweight Pre-trained CNN networks used in the present work7.5: Decision fusion7.6: Experiments and results7.7: Concluding remarks
Chapter 8: Hybrid computer-aided classification system design using lightweight end-to-end Pre-trained CNN-based deep feature extraction and ANFC-LH classifier for chest radiographs
Abstract8.1: Introduction8.2: Experimental workflow8.3: Deep feature extraction8.4: Feature selection8.5: Adaptive neuro-fuzzy classifier8.6: Experiment and results8.7: Concluding remarks
Chapter 9: Hybrid computer-aided classification system design using lightweight end-to-end Pre-trained CNN-based deep feature extraction and PCA-SVM classifier for chest radiographs
Abstract9.1: Introduction9.2: Experimental workflow9.3: Deep feature extraction9.4: Feature selection and dimensionality reduction9.5: SVM classifier9.6: Experiment and results9.7: Concluding remarks
Chapter 10: Comparative analysis of computer-aided classification systems designed for chest radiographs: Conclusion and future scope
Abstract10.1: Introduction10.2: Conclusion: End-to-end pretrained CNN-based CAC system design for chest radiographs10.3: Conclusion: Hybrid CAC system design using end-to-end pretrained CNN-based deep feature extraction and ANFC-LH, PCA-SVM classifiers for chest radiographs10.4: Conclusion: Lightweight end-to-end pretrained CNN-based CAC system design for chest radiographs10.5: Conclusion: Hybrid CAC system design using lightweight end-to-end pretrained CNN-based deep feature extraction and ANFC-LH, PCA-SVM classifiers for chest radiographs10.6: Comparison of the different CNN-based CAC systems designed in the present work for the binary classification of chest radiographs10.7: Future scope
Index

Content preview from Deep Learning for Chest Radiographs

Chapter 7: Lightweight end-to-end Pre-trained CNN-based computer-aided classification system design for chest radiographs

Abstract

In this chapter there is an exhaustive description of the experiments carried out for the design of lightweight end-to-end Pre-trained CNN-based CAC systems for chest radiographs. It explains in detail the architectural composition of the lightweight Pre-trained CNN model SqueezeNet, ShuffleNet, and MobileNetv2 used for carrying out the experiments. The code snippets of the different experiments aim at giving a better understanding to the programmatic implementation of designing these CAC systems.

Keywords

Lightweight CNN; DAG network; SqueezeNet; ShuffleNet; MobileNetV2; Transfer learning; Decision fusion

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