Deep Learning for Chest Radiographs
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portes grátis
Deep Learning for Chest Radiographs
Computer-Aided Classification
Kumar, Papendra; Virmani, Jitendra; Bhadauria, H.S; Chandola, Yashvi
Elsevier Science & Technology
07/2021
228
Mole
Inglês
9780323901840
15 a 20 dias
500
Descrição não disponível.
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
<P>Deep learning, Machine learning, Chest radiographs, Convolution neural network (CNN), Pneumonia, Computer-aided classification (CAC) system, Series networks, DAG networks, Transfer learning, AlexNet, GoogLeNet, ResNet18, SqueezeNet, ShuffleNet, MobileNetV2, ANFC-LH Classifier, PCA-SVM Classifier, Decision Fusion, Deep feature extraction, Feature selection, feature dimensionality reduction, Correlation based feature Selection, Principal Component Analysis.</P>
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
<P>Deep learning, Machine learning, Chest radiographs, Convolution neural network (CNN), Pneumonia, Computer-aided classification (CAC) system, Series networks, DAG networks, Transfer learning, AlexNet, GoogLeNet, ResNet18, SqueezeNet, ShuffleNet, MobileNetV2, ANFC-LH Classifier, PCA-SVM Classifier, Decision Fusion, Deep feature extraction, Feature selection, feature dimensionality reduction, Correlation based feature Selection, Principal Component Analysis.</P>
