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

Read now

Unlock full access

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 1: Introduction

Abstract

This chapter highlights the motivation behind designing computer-aided classification (CAC) systems for chest radiographs. It gives a brief overview of the concepts of artificial intelligence, deep learning, medical images, medical imaging techniques, and diagnostic features of medical images. The chapter discusses the different medical imaging techniques beneficial in diagnosis of chest abnormalities, primarily pneumonia, and the diagnostic features that a medical practitioner looks for to identify normal, pneumonia, and COVID19 chest radiographs.

Keywords

Pneumonia; Deep learning; Artificial intelligence; Medical images; Medical imaging techniques; Chest radiograph; Diagnostic features; Convolutional neural ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

State-of-the-Art Deep Learning Models in TensorFlow: Modern Machine Learning in the Google Colab Ecosystem

Publisher Resources

ISBN: 9780323906869

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design