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Deep learning for chest radiographs : computer-aided classification /
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." Pneu...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press,
2021.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 020 | |a 9780323906869 |q (electronic bk.) | ||
| 020 | |a 0323906869 |q (electronic bk.) | ||
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| 245 | 0 | 0 | |a Deep learning for chest radiographs : |b computer-aided classification / |c Yashvi Chandola [and more]. |
| 264 | 1 | |a London : |b Academic Press, |c 2021. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 520 | |a 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. 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. | ||
| 650 | 0 | |a Chest |x Radiography. | |
| 650 | 0 | |a Artificial intelligence |x Medical applications. | |
| 650 | 0 | |a Pneumonia |x Diagnosis. | |
| 650 | 0 | |a Pneumonia |x Imaging. | |
| 650 | 6 | |a Thorax |x Radiographie. |0 (CaQQLa)201-0051365 | |
| 650 | 6 | |a Intelligence artificielle en m�edecine. |0 (CaQQLa)201-0180593 | |
| 650 | 6 | |a Pneumonie |0 (CaQQLa)201-0027510 |x Imagerie. |0 (CaQQLa)201-0377501 | |
| 650 | 7 | |a Artificial intelligence |x Medical applications |2 fast |0 (OCoLC)fst00817267 | |
| 650 | 7 | |a Chest |x Radiography |2 fast |0 (OCoLC)fst00853845 | |
| 650 | 7 | |a Pneumonia |x Diagnosis |2 fast |0 (OCoLC)fst01067564 | |
| 700 | 1 | |a Chandola, Yashvi. | |
| 776 | 0 | 8 | |i Print version: |t Deep learning for chest radiographs. |d London : Academic Press, 2021 |z 0323901840 |z 9780323901840 |w (OCoLC)1231959050 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780323901840 |z Texto completo |