Handbook of deep learning in biomedical engineering : techniques and applications /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Balas, Valentina Emilia (Editor ), Mishra, Brojo Kishore, 1979- (Editor ), Kumar, Raghvendra, 1987- (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Academic Press, 2021.
Temas:
Artificial intelligence > Medical applications.
Biomedical engineering > Information technology.
Machine learning.
Machine Learning
Intelligence artificielle en m�edecine.
G�enie biom�edical > Technologie de l'information.
Apprentissage automatique.
Artificial intelligence > Medical applications
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • HANDBOOK OF DEEP LEARNING IN BIOMEDICAL ENGINEERING
  • HANDBOOK OF DEEP LEARNING IN BIOMEDICAL ENGINEERING
  • Copyright
  • Contents
  • Contributors
  • About the editors
  • Preface
  • Key features
  • About the book
  • 1
  • Congruence of deep learning in biomedical engineering: future prospects and challenges
  • 1. Introduction
  • 1.1 SqueezeNet (image classification)
  • 1.1.1 Strategies of architectural design
  • 2. Fire module
  • 3. Background study
  • 3.1 Need of security
  • 3.1.1 Types of security methods
  • 3.1.1.1 Steganography
  • 3.1.1.2 Watermarking
  • 3.1.1.3 Cryptography
  • 3.2 Advantages of steganography over cryptography
  • 3.2.1 Resolution of steganography
  • 3.2.2 Types of steganography
  • 3.2.3 Image steganography
  • 3.2.4 Image steganography method
  • 3.3 Steganography techniques
  • 3.3.1 Spatial domain technique
  • 3.3.1.1 Least significant bit technique
  • 3.3.2 Transform domain technique
  • 3.4 Advantages of transform domain over spatial domain
  • 3.5 Related study
  • 3.5.1 DWT based
  • 3.5.2 IWT based
  • 3.6 Advantages of IWT over DWT
  • 4. Study of various types of model
  • 5. Proposed method by the authors
  • 5.1 2D Haar wavelet transform
  • 5.2 Huffman encoding technique
  • 5.3 Embedding algorithm
  • 6. Conclusion and future work
  • References
  • 2
  • Deep convolutional neural network in medical image processing
  • 1. Introduction
  • 2. Medical image analysis
  • 2.1 Segmentation
  • 2.2 Detection or diagnosis by computer-aided system
  • 2.3 Detection and classification of abnormality
  • 2.4 Registration
  • 3. Convolutional neural network and its architectures
  • 3.1 Architectures of deep convolutional neural network
  • 3.1.1 General classification architectures
  • 3.1.2 Multistream architectures
  • 3.1.3 Segmentation architectures
  • 4. Application of deep convolutional neural network in medical image analysis
  • 4.1 Brain
  • 4.2 Eye
  • 4.3 Breast
  • 4.4 Chest
  • 4.5 Cardiac
  • 4.6 Abdomen
  • 5. Critical discussion: inferences for future work and limitations
  • 6. Conclusion
  • References
  • 3
  • Application, algorithm, tools directly related to deep learning
  • 1. Introduction
  • 2. Tools used in deep learning
  • 2.1 TensorFlow
  • 2.1.1 Tensor data structure
  • 2.1.2 Rank
  • 2.1.3 Shape
  • 2.1.4 Type
  • 2.1.5 One-dimensional Tensor
  • 2.1.6 Two-dimensional Tensor
  • 2.2 Keras
  • 2.2.1 Backend in Keras
  • 2.2.2 Installing keras: Amazon Web Service
  • 2.3 CAFFE
  • 2.3.1 The main features of CAFFE
  • 2.4 Torch tool
  • 2.5 Theano
  • 3. Algorithms
  • 3.1 Deep belief networks
  • 3.1.1 Architecture of Deep belief network
  • 3.1.2 Working of deep belief network
  • 3.2 Convolutional neural network
  • 3.2.1 Input image
  • 3.2.2 Convolution layer-the kernel
  • 3.3 Recurrent neural network
  • 3.3.1 How recurrent neural network works
  • 3.4 Long short-term memory networks
  • 3.4.1 Structure of long short-term memory
  • 3.5 Stacked autoencoders
  • 3.6 Deep Boltzmann Machine
  • 4. Applications of deep learning

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