Deep Learning for Chest Radiographs
-10%
portes grátis
Deep Learning for Chest Radiographs
Computer-Aided Classification
Chandola, Yashvi; Bhadauria, H.S; Virmani, Jitendra; Kumar, Papendra
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
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
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>
