State of the art in neural networks and their applications. Volume 1 /

Mostrar otras versiones (1)
Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: El-Baz, Ayman S., Suri, Jasjit S.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2021.
Temas:
Neural networks (Computer science)
Diagnostic imaging > Data processing.
Image analysis.
Neural Networks, Computer
R�eseaux neuronaux (Informatique)
Imagerie pour le diagnostic > Informatique.
Analyse d'images.
Diagnostic imaging > Data processing
Image analysis
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • State of the Art in Neural Networks and Their Applications
  • Copyright Page
  • Dedication
  • Contents
  • List of Contributors
  • Biographies
  • Acknowledgments
  • 1 Computer-aided detection of abnormality in mammography using deep object detectors
  • 1.1 Introduction
  • 1.2 Literature review
  • 1.3 Methodology
  • 1.3.1 Architectures of deep convolutional neural networks and deep object detectors
  • 1.3.2 Abnormality detection with faster R-convolutional neural networks
  • 1.3.3 Abnormality detection with YOLO
  • 1.4 Experimental results
  • 1.4.1 Data preparation
  • 1.4.2 Abnormality detection with faster R-convolutional neural networks
  • 1.4.3 Abnormality detection with YOLO
  • 1.4.4 Results comparison
  • 1.5 Discussions
  • 1.6 Conclusion
  • References
  • 2 Detection of retinal abnormalities in fundus image using CNN deep learning networks
  • 2.1 Introduction
  • 2.2 Earlier screening and diagnosis of ocular diseases with CNN deep learning networks
  • 2.2.1 Glaucoma
  • 2.2.1.1 Methods and materials
  • 2.2.1.2 Deep learning neural-network architectures for glaucoma screening and diagnosis
  • 2.2.1.3 Application and evaluation on earlier glaucoma screening and diagnosis-classification
  • 2.2.1.3.1 Fundus image glaucoma classification
  • 2.2.1.3.2 Optical coherence tomography image glaucoma classification
  • 2.2.1.4 Datasets used in glaucoma diagnosis
  • 2.2.2 Age-related macular degeneration
  • 2.2.2.1 Methods and materials
  • 2.2.2.2 Deep learning-based methods for age-related macular degeneration detection and grading
  • 2.2.3 Diabetic retinopathy
  • 2.2.3.1 Methods and materials
  • 2.2.3.2 Deep learning-based methods for diabetic retinopathy detection and grading
  • 2.2.3.3 Dataset used diabetic retinopathy diagnosis
  • 2.2.4 Cataract
  • 2.2.4.1 Methods and materials
  • 2.2.4.2 Deep learning-based methods for cataract detection and grading
  • 2.3 Deep learning-based smartphone for detection of retinal abnormalities
  • 2.3.1 Smartphone-captured fundus image evaluation
  • 2.3.2 Deep learning-based method of ocular pathology detection from smartphone-captured fundus image
  • 2.4 Discussion
  • 2.5 Conclusion
  • References
  • 3 A survey of deep learning-based methods for cryo-electron tomography data analysis
  • 3.1 Introduction
  • 3.2 Deep learning-based methods
  • 3.2.1 Detection and segmentation
  • 3.2.2 Classification
  • 3.2.3 Others
  • 3.3 Conclusion
  • References
  • 4 Detection, segmentation, and numbering of teeth in dental panoramic images with mask regions with convolutional neural ne...
  • 4.1 Introduction
  • 4.2 Related work
  • 4.3 F�ed�eration Dentaire Internationale tooth numbering system
  • 4.4 The method
  • 4.4.1 Implementation details
  • 4.4.1.1 Tooth numbering
  • 4.5 Experimental analysis
  • 4.5.1 Dataset
  • 4.5.2 Evaluation
  • 4.5.3 Results
  • 4.6 Discussion and conclusions
  • References

Ejemplares similares