Deep Learning for Chest Radiographs
Computer-Aided Classification
- 1st Edition - July 16, 2021
- Authors: Yashvi Chandola, Jitendra Virmani, H.S Bhadauria, Papendra Kumar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 1 8 4 - 0
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 6 8 6 - 9
Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classific… Read more
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Request a sales quoteDeep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially in developing countries. Out of all the available imaging modalities, such as computed tomography, radiography or X-ray, magnetic resonance imaging, ultrasound, and so on, chest radiographs are most widely used for differential diagnosis between Normal and Pneumonia. In the CAC system designs implemented in this book, a total of 200 chest radiograph images consisting of 100 Normal images and 100 Pneumonia images have been used. These chest radiographs are augmented using geometric transformations, such as rotation, translation, and flipping, to increase the size of the dataset for efficient training of the Convolutional Neural Networks (CNNs). A total of 12 experiments were conducted for the binary classification of chest radiographs into Normal and Pneumonia. It also includes in-depth implementation strategies of exhaustive experimentation carried out using transfer learning-based approaches with decision fusion, deep feature extraction, feature selection, feature dimensionality reduction, and machine learning-based classifiers for implementation of end-to-end CNN-based CAC system designs, lightweight CNN-based CAC system designs, and hybrid CAC system designs for chest radiographs.
This book is a valuable resource for academicians, researchers, clinicians, postgraduate and graduate students in medical imaging, CAC, computer-aided diagnosis, computer science and engineering, electrical and electronics engineering, biomedical engineering, bioinformatics, bioengineering, and professionals from the IT industry.
- Provides insights into the theory, algorithms, implementation, and application of deep-learning techniques for medical images such as transfer learning using pretrained CNNs, series networks, directed acyclic graph networks, lightweight CNN models, deep feature extraction, and conventional machine learning approaches for feature selection, feature dimensionality reduction, and classification using support vector machine, neuro-fuzzy classifiers
- Covers the various augmentation techniques that can be used with medical images and the CNN-based CAC system designs for binary classification of medical images focusing on chest radiographs
- Investigates the development of an optimal CAC system design with deep feature extraction and classification of chest radiographs by comparing the performance of 12 different CAC system designs
Researchers, graduate students in Computer science and engineering, Electrical Engineering, Biomedical engineering, bioinformatics and professionals from the industry
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface
- Acknowledgments
- Chapter 1: Introduction
- Abstract
- 1.1: Motivation
- 1.2: Introduction to deep learning
- 1.3: Why deep learning in medical image analysis?
- 1.4: Medical imaging
- 1.5: Description of normal and pneumonia chest radiographs
- 1.6: Objective of the book
- 1.7: Book chapter outline
- Chapter 2: Review of related work
- Abstract
- 2.1: Introduction
- 2.2: Overview of the studies based on the classification of chest radiographs
- 2.3: Concluding remarks
- Chapter 3: Methodology adopted for designing of computer-aided classification systems for chest radiographs
- Abstract
- 3.1: Introduction
- 3.2: What is a CAC system?
- 3.3: Need for CAC systems
- 3.4: Need for CAC systems for chest radiographs
- 3.5: Types of classifier designs for CAC systems
- 3.6: Deep learning-based CAC system design
- 3.7: Workflow adopted in the present work
- 3.8: Implementation details
- 3.9: Dataset: Kaggle chest X-ray dataset
- 3.10: Dataset description
- 3.11: Dataset generation
- 3.12: Concluding remarks
- Chapter 4: End-to-end pre-trained CNN-based computer-aided classification system design for chest radiographs
- Abstract
- 4.1: Introduction
- 4.2: Experimental workflow
- 4.3: Transfer learning-based convolutional neural network design
- 4.4: Architecture of end-to-end pre-trained CNNs used in the present work
- 4.5: Decision fusion
- 4.6: Experiments and results
- 4.7: 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
- Abstract
- 5.1: Introduction
- 5.2: Experimental workflow
- 5.3: Deep feature extraction
- 5.4: Feature selection
- 5.5: Adaptive neuro-fuzzy classifier
- 5.6: Experiment and result
- 5.7: 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
- Abstract
- 6.1: Introduction
- 6.2: Experimental workflow
- 6.3: Deep feature extraction
- 6.4: Feature selection and dimensionality reduction
- 6.5: SVM classifier
- 6.6: Experiment and result
- 6.7: Concluding remarks
- Chapter 7: Lightweight end-to-end Pre-trained CNN-based computer-aided classification system design for chest radiographs
- Abstract
- 7.1: Introduction
- 7.2: Experimental workflow
- 7.3: Lightweight CNN model
- 7.4: Architecture of lightweight Pre-trained CNN networks used in the present work
- 7.5: Decision fusion
- 7.6: Experiments and results
- 7.7: 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
- Abstract
- 8.1: Introduction
- 8.2: Experimental workflow
- 8.3: Deep feature extraction
- 8.4: Feature selection
- 8.5: Adaptive neuro-fuzzy classifier
- 8.6: Experiment and results
- 8.7: 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
- Abstract
- 9.1: Introduction
- 9.2: Experimental workflow
- 9.3: Deep feature extraction
- 9.4: Feature selection and dimensionality reduction
- 9.5: SVM classifier
- 9.6: Experiment and results
- 9.7: Concluding remarks
- Chapter 10: Comparative analysis of computer-aided classification systems designed for chest radiographs: Conclusion and future scope
- Abstract
- 10.1: Introduction
- 10.2: Conclusion: End-to-end pretrained CNN-based CAC system design for chest radiographs
- 10.3: Conclusion: Hybrid CAC system design using end-to-end pretrained CNN-based deep feature extraction and ANFC-LH, PCA-SVM classifiers for chest radiographs
- 10.4: Conclusion: Lightweight end-to-end pretrained CNN-based CAC system design for chest radiographs
- 10.5: Conclusion: Hybrid CAC system design using lightweight end-to-end pretrained CNN-based deep feature extraction and ANFC-LH, PCA-SVM classifiers for chest radiographs
- 10.6: Comparison of the different CNN-based CAC systems designed in the present work for the binary classification of chest radiographs
- 10.7: Future scope
- Index
- No. of pages: 228
- Language: English
- Edition: 1
- Published: July 16, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780323901840
- eBook ISBN: 9780323906869
YC
Yashvi Chandola
Yashvi Chandola received her B-Tech (Hons.) in Computer Science and Engineering from Women Institute of Technology, Dehradun, Uttarakhand in 2018. She has completed her M-Tech (Hons.) in Computer Science and Engineering from Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand in 2020. Her research interests include application of machine learning and deep learning algorithms for analysis of medical images.
Affiliations and expertise
Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, UttarakhandJV
Jitendra Virmani
Jitendra Virmani received his B-Tech (Hons.) in Instrumentation Engineering from Sant Longowal Institute of Engineering and Technology, Punjab in 1999 and M-Tech in Electrical Engineering with specialization in Measurement and Instrumentation from the Indian Institute of Technology, Roorkee in 2006. He served in the academia for nine years before joining the PhD programme in 2009 at Biomedical Instrumentation Laboratory, Electrical Engineering Department, Indian Institute of Technology, Roorkee as a full time Research Scholar under MHRD Assistantship. He received his PhD from the Indian Institute of Technology, Roorkee in 2013. After his PhD he served in Academia for Jaypee University of Information Technology, Solan, Himachal Pradesh and Thapar Institute of Engineering and Technology, Patiala, Punjab before joining CSIR-CSIO, Chandigarh in August 2016. He is presently working at CSIR-CSIO, Chandigarh. He is a Life member of the Institute of Engineers (IEI), India and Computer Society of India. He has published 85 papers in various journals, conferences and Book chapters with various reputed publishers. He has delivered more than 35 expert talks on various platforms basically on application of machine learning and deep learning algorithms for medical images. He is Editorial Board Member of International Journal of Image Mining published by Inderscience Publishers. His research interests include application of machine learning and deep learning algorithms for analysis of medical images.
Affiliations and expertise
CSIR-CSIO, Chandigarh, Institute of Engineers (IEI), IndiaHB
H.S Bhadauria
H.S Bhadauria received his B-Tech in Computer Science and Engineering from Aligarh Muslim University, Aligarh in 1999, and M-Tech in Electronics Engineering from Aligarh Muslim University, Aligarh in 2004. He received his PhD on Detection and Segmentation of Brain Hemorrhage using CT images from Biomedical Signal and Image Processing Laboratory, Indian Institute of Technology - Roorkee in 2013. During his PhD he worked on enhancing the detection and segmentation of brain hemorrhage using CT imaging modality. He has served in academia for more than 12 years. He is presently serving as a Professor in the Department of Computer Science & Engineering at Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand. He is a life member of Institute of Engineers (IEI), India. He has published more than 60 research papers in International and National Journals and Conferences. His areas of research interest are Digital Image and Digital Signal Processing.
Affiliations and expertise
Professor in the Department of Computer Science & Engineering at Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand.
Institute of Engineers (IEI), IndiaPK
Papendra Kumar
Papendra Kumar received his B.E in Computer Science and Engineering from Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand in 2007 and M-Tech in Digital Signal Processing from Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand in 2011. He is presently serving as an Assistant Professor in the Department of Computer Science & Engineering at Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand. His areas of research interest are Digital Image and Digital Signal Processing.
Affiliations and expertise
Assistant Professor in the Department of Computer Science & Engineering at Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, UttarakhandRead Deep Learning for Chest Radiographs on ScienceDirect