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SCIDIR_on1223025978 |
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OCoLC |
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20231120010520.0 |
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200910s2021 enka fo 000 0 eng d |
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|a UKAHL
|b eng
|e rda
|e pn
|c UKAHL
|d EBLCP
|d OCLCO
|d OPELS
|d OCLCO
|d UPM
|d OCLCF
|d VRC
|d OCLCO
|d OCLCQ
|d OCLCO
|d SFB
|d OCLCQ
|d OCLCO
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| 020 |
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|a 9780128230473
|q (e-book)
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|a 0128230479
|q (e-book)
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|z 9780128230145
|q (print)
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| 035 |
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|a (OCoLC)1223025978
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| 050 |
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4 |
|a R859.7.A78
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| 082 |
0 |
4 |
|a 610.285631
|2 23
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| 245 |
0 |
0 |
|a Handbook of deep learning in biomedical engineering :
|b techniques and applications /
|c edited by Valentina E. Balas, Brojo Kishore Mishra, Raghvendra Kumar.
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| 264 |
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1 |
|a London :
|b Academic Press,
|c 2021.
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| 300 |
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|a 1 online resource :
|b illustrations (black and white, and colour)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 505 |
0 |
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|a 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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| 505 |
8 |
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|a 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
|
| 505 |
8 |
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|a 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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| 505 |
8 |
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|a 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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| 505 |
8 |
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|a 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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| 650 |
|
0 |
|a Artificial intelligence
|x Medical applications.
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| 650 |
|
0 |
|a Biomedical engineering
|x Information technology.
|
| 650 |
|
0 |
|a Machine learning.
|
| 650 |
|
2 |
|a Machine Learning
|0 (DNLM)D000069550
|
| 650 |
|
6 |
|a Intelligence artificielle en m�edecine.
|0 (CaQQLa)201-0180593
|
| 650 |
|
6 |
|a G�enie biom�edical
|0 (CaQQLa)201-0021888
|x Technologie de l'information.
|0 (CaQQLa)201-0379284
|
| 650 |
|
6 |
|a Apprentissage automatique.
|0 (CaQQLa)201-0131435
|
| 650 |
|
7 |
|a Artificial intelligence
|x Medical applications
|2 fast
|0 (OCoLC)fst00817267
|
| 650 |
|
7 |
|a Machine learning
|2 fast
|0 (OCoLC)fst01004795
|
| 700 |
1 |
|
|a Balas, Valentina Emilia,
|e editor.
|
| 700 |
1 |
|
|a Mishra, Brojo Kishore,
|d 1979-
|e editor.
|
| 700 |
1 |
|
|a Kumar, Raghvendra,
|d 1987-
|e editor.
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780128230145
|z Texto completo
|
