Deep Learning for Chest Radiographs
Computer-Aided Classification
Published on 22. July 2021
Book
Paperback/Softback
228 pages
978-0-323-90184-0 (ISBN)
Description
Deep 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.
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.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
College/higher education
Product notice
Paperback (trade)
Illustrations
60 illustrations (20 in full color); Illustrations
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 12 mm
Weight
401 gr
ISBN-13
978-0-323-90184-0 (9780323901840)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Yashvi Chandola | Jitendra Virmani | H. S. Bhadauria
Deep Learning for Chest Radiographs
Computer-Aided Classification
E-Book
07/2021
Academic Press
€86.95
Available for download
Persons
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. 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. 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. 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.
Author
Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand
CSIR-CSIO, Chandigarh, Institute of Engineers (IEI), India
Professor in the Department of Computer Science & Engineering at Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand.
Institute of Engineers (IEI), India
Institute of Engineers (IEI), India
Assistant Professor in the Department of Computer Science & Engineering at Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand
Content
1. Introduction
2. Review of Related Work
3. Methodology Adopted for Designing of Computer-Aided Classification Systems for Chest Radiographs
4. End-to-end Pre-trained CNN-based Computer-Aided Classification System design for Chest Radiographs
5. Hybrid Computer-Aided Classification System Design Using End-to-end Pre-trained CNN-based Deep Feature Extraction and ANFC-LH Classifier for Chest Radiographs
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
7. Light-weight End-to-end Pre-trained CNN-based Computer-Aided Classification System Design for Chest Radiographs
8. Hybrid Computer-Aided Classification System Design Using Light-weight End-to-end Pre-trained CNN-based Deep Feature Extraction and ANFC-LH Classifier for Chest Radiographs
9. Hybrid Computer-Aided Classification System Design Using Light-weight End-to-end Pre-trained CNN-based Deep Feature Extraction and PCA-SVM Classifier for Chest Radiographs
10. Comparative Analysis of Computer-Aided Classification Systems Designed for Chest Radiographs: Conclusion and Future Scope
2. Review of Related Work
3. Methodology Adopted for Designing of Computer-Aided Classification Systems for Chest Radiographs
4. End-to-end Pre-trained CNN-based Computer-Aided Classification System design for Chest Radiographs
5. Hybrid Computer-Aided Classification System Design Using End-to-end Pre-trained CNN-based Deep Feature Extraction and ANFC-LH Classifier for Chest Radiographs
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
7. Light-weight End-to-end Pre-trained CNN-based Computer-Aided Classification System Design for Chest Radiographs
8. Hybrid Computer-Aided Classification System Design Using Light-weight End-to-end Pre-trained CNN-based Deep Feature Extraction and ANFC-LH Classifier for Chest Radiographs
9. Hybrid Computer-Aided Classification System Design Using Light-weight End-to-end Pre-trained CNN-based Deep Feature Extraction and PCA-SVM Classifier for Chest Radiographs
10. Comparative Analysis of Computer-Aided Classification Systems Designed for Chest Radiographs: Conclusion and Future Scope