Deep Learning for Chest Radiographs: Computer-Aided Classification: Primers in Biomedical Imaging Devices and Systems
Autor Yashvi Chandola, Jitendra Virmani, H.S Bhadauria, Papendra Kumaren Limba Engleză Paperback – 21 iul 2021
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
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Specificații
ISBN-13: 9780323901840
ISBN-10: 0323901840
Pagini: 228
Ilustrații: 60 illustrations (20 in full color)
Dimensiuni: 191 x 235 mm
Greutate: 0.4 kg
Editura: ELSEVIER SCIENCE
Seria Primers in Biomedical Imaging Devices and Systems
ISBN-10: 0323901840
Pagini: 228
Ilustrații: 60 illustrations (20 in full color)
Dimensiuni: 191 x 235 mm
Greutate: 0.4 kg
Editura: ELSEVIER SCIENCE
Seria Primers in Biomedical Imaging Devices and Systems
Cuprins
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