| Description |
1 online resource : illustrations (black and white, and colour) |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Subject |
Artificial intelligence -- Medical applications.
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Biomedical engineering -- Information technology
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Machine learning.
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Machine Learning
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Artificial intelligence -- Medical applications
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Machine learning
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| Form |
Electronic book
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| Author |
Balas, Valentina Emilia, editor.
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Mishra, Brojo Kishore, 1979- editor.
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Kumar, Raghvendra, 1987- editor.
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| ISBN |
9780128230473 |
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0128230479 |
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