Deep Learning for Chest Radiographs

Deep Learning for Chest Radiographs proposes the design of a convolution neural network-based computer-aided diagnostic (CAD) system for diagnosis of pneumonia using chest x-ray images. A total of 200 chest x-ray images (100 normal and 100 pneumonia) are used. The chest radiographs are augmented using geometric transformations such as rotation, translation, and flipping to increase the dataset's size. A total of 12 experiments have been conducted; each of the experiments deals in designing a separate CAD system for the binary classification of chest radiographs. The book contains a comprehensive comparison of various deep feature extraction and classification-based CAD designs for chest radiographs. It also includes an in-depth implementation strategy for data augmentation, data resizing, transfer learning, and deep feature extraction and classification methods for chest radiographs. This book is a valuable resource for academicians, researchers, clinicians, post-graduate/graduate students in Medical Imaging, Computer-Aided Diagnosis, Computer Science and Engineering, Electrical & 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 image such as transfer learning, pre-trained convolution neural networks, series networks, DAG networks, lightweight CNN models, feature dimensionality reduction, deep feature extraction, support vector machine, neuro-fuzzy classifiersCovers the various augmentation techniques that can be used with medical images and the CNN based CAD system designs for binary classification of medical images focusing on chest radiographsInvestigates the development of optimal CAD system design with deep feature extraction and classification of chest radiographs by comparing the performance of 12 different CAD system designs INDICE: 1. Introduction 2. Review of Related Work 3. Methodology Adopted for Designing of CAD systems for Chest radiographs 4. CAD system Designs for chest radiographs based on Series and DAG CNN Models 5. CAD system design for chest radiographs using Deep feature extraction by GoogLeNet and ANFC-LH classifier 6. CAD system design for chest radiographs using Deep feature extraction by GoogLeNet and PCA-SVM classifier 7. CAD system design for chest radiographs using lightweight DAG CNN model 8. CAD system design for chest radiographs using Deep feature extraction by lightweight MobileNetV2 CNN model and ANFC-LH classifier 9. CAD system design for chest radiographs using Deep feature extraction by lightweight MobileNetV2 CNN model and PCA-SVM classifier 10. Comparative Analysis of CAD systems designed for Chest Radiographs: Conclusion and Future Scope

• ISBN: 978-0-323-90184-0
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 200
• Fecha Publicación: 01/08/2021
• Nº Volúmenes: 1
• Idioma: Inglés