Handbook of Deep Learning in Biomedical Engineering: Techniques and Applications

HANDBOOK OF DEEPLEARNING IN BIOMEDICAL ENGINEERING
Copyright
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
	5. Conclusion
	References
4 . A critical review on using blockchain technology in education domain
	1. Introduction
	2. Consortium blockchain and its suitability for e-governance
	3. Consensus
		3.1 Proof approaches
		3.2 Vote-based approaches
		3.3 Directed acyclic graph approaches
	4. Attacks on blockchain
	5. Blockchain in education domain
	6. Scalability challenges
	7. Security challenges
	8. Conclusion
	References
	Further reading
5 . Depression discovery in cancer communities using deep learning
	1. Introduction
	2. Related work
		2.1 Lexicon-based approaches
		2.2 Machine learning–based approaches
			2.2.1 Supervised machine learning
			2.2.2 Sentiment analysis using supervised machine learning
			2.2.3 Metaclassifiers
		2.3 Hybrid approaches
		2.4 Other techniques
		2.5 Sentiment analysis for online depression detection
	3. Proposed system architecture
		3.1 Continuous bag of words
			3.1.1 Skip-gram model
			3.1.2 Word embedding optimization
	4. Models
		4.1 Convolutional neural network
			4.1.1 Variants of convolutional neural network
		4.2 Recurrent neural network
		4.3 Long short-term memory
			4.3.1 Bidirectional long short-term memory
	5. Conclusion
	References
	Further reading
6 . Plant leaf disease classification based on feature selection and deep neural network
	1. Introduction
	2. Literature review
		2.1 Plant diseases recognition using convolutional neural networks
		2.2 Plant diseases recognition with artificial neural network
		2.3 Feature selection
	3. Our proposed framework
		3.1 Data set
		3.2 Image preprocessing
		3.3 Convolutional neural network
			3.3.1 AlexNet (2012)
			3.3.2 VGG16 (2014)
			3.3.3 ResNet (2015)
	4. Results
		4.1 Conventional models
		4.2 Models with transfer learning
		4.3 Multilayer perceptron approach
			4.3.1 Feature extraction
			4.3.2 Feature selection
				4.3.2.1 Particle swarm optimization
				4.3.2.2 Gray wolf optimization
				4.3.2.3 Proposed adaptive particle–gray wolf optimization heuristic
				4.3.2.4 Wrapper-based adaptive particle–gray wolf optimization
	5. Conclusion
	References
	Further reading
7 . Early detection and diagnosis using deep learning
	1. Introduction
		1.1 Introduction to deep learning
			1.1.1 Applications
			1.1.2 Challenges faced by deep learning
		1.2 Introduction to biomedical engineering
			1.2.1 Branches of biomedical engineering
			1.2.2 Challenges faced by biomedical engineering
	2. Diagnostics using deep learning
		2.1 Motivation for use of deep learning in diagnostics
		2.2 Challenges and solutions
			2.2.1 Retroactive versus forthcoming trainings
			2.2.2 Metric cannot be used for medical applicability
			2.2.3 Trouble associating dissimilar algorithms
			2.2.4 Hominoid barricades to artificial intelligence acceptance in medical sector
			2.2.5 Vulnerability to confrontational occurrence or management
		2.3 Future of diagnostics using deep learning
	3. Early detection of diseases using deep learning
		3.1 Rheumatic diseases
		3.2 Alzheimer's disease
		3.3 Autism spectrum disorder
		3.4 Attention deficit hyperactivity disorder
	4. Conclusion and further advancements
	References
8 . A review on plant diseases recognition through deep learning
	1. Introduction
	2. Plant diseases
	3. Traditional methods to treat plant diseases
		3.1 Serological assays
			3.1.1 Modern serological methods
				3.1.1.1 Enzyme-linked immunosorbent assay
				3.1.1.2 Dot blot immunobinding assay
				3.1.1.3 Tissue blotting immunoassay
		3.2 Nuclei acid–based methods
			3.2.1 Polymerase chain reaction
			3.2.2 Restriction fragment length polymorphisms
			3.2.3 Amplified fragment length polymorphism
	4. Innovative detection method
		4.1 Lateral flow microarrays
		4.2 Methods based on the analysis of volatile compounds as biomarkers
	5. Remote sensing of plant diseases
		5.1 Detection of plant impairment using remote sensing systems
		5.2 Remote sensing systems for monitoring pests and diseases
		5.3 Visible and short-wave infrared monitoring systems
		5.4 Fluorescence and thermal sensors
	6. Plant disease detection by well-known deep learning architectures
		6.1 Evolution of Deep learning
		6.2 Without visualization technique
		6.3 Visualization techniques
		6.4 Hyperspectral imaging with deep learning models
	7. Conclusions
	References
9 . Applications of deep learning in biomedical engineering
	1. Introduction
	2. Biomedical engineering
	3. Deep learning
	4. Most popular deep neural networks architectures used in biomedical applications
	5. Convolutional neural network
	6. Convolution layer
	7. Pooling layer
	8. Fully convolutional layer
	9. Applications of convolutional neural network in biomedicine
	10. Recurrent neural network
	11. Applications of recurrent neural network in biomedicine
	12. Generative adversarial networks
		12.1 Generator network
		12.2 Discriminator network
	13. Applications of generative adversarial network in biomedicine
	14. Deep belief network
	15. Pretraining stage
	16. Fine-tuning stage
	17. Applications of deep learning in biomedicine
	18. Biomedical image analysis
	19. Image detection and recognition
	20. Image acquisition and image interpretation
	21. Image segmentation
	22. Cytopathology and histopathology
	23. Brain, body, and machine interface
		23.1 Brain–machine interface
	24. Classification of the brain–machine interfaces
	25. Invasive techniques
	26. Noninvasive techniques
	27. Body–machine interface
	28. Drug infusion system
	29. Rehabilitation system
	30. Diseases diagnosis
	31. Omics
	32. Around the genome
	33. Protein-binding prediction
	34. DNA–RNA-binding proteins
	35. Gene expression
	36. Alternative splicing
	37. Gene expression prediction
	38. Genomic sequencing
	39. Around the protein
	40. Protein Structure Prediction
	41. Protein secondary structure prediction
		41.1 Protein tertiary structure prediction
		41.2 Protein quality assessment
		41.3 Protein loop modeling and disorder prediction
	42. Protein Interaction Prediction
		42.1 Protein–protein interactions
		42.2 Drug–target interactions
	43. Public and medical health management
	44. Conclusion
	References
10 . Deep neural network in medical image processing
	1. Literature review
	2. Digital image and computer vision
		2.1 Introduction
		2.2 What is an image?
		2.3 Digital representation of an image
		2.4 Medical image formats
		2.5 Steps in image processing
		2.6 Machine learning and its types
		2.7 Unlabeled data set
		2.8 Labeled data set
		2.9 Supervised learning
		2.10 Unsupervised learning
		2.11 Reinforcement learning
		2.12 Artificial neural network
	3. Deep learning
		3.1 Deep learning architectures
	4. Segmentation techniques in image processing
		4.1 Different approaches for segmentation
		4.2 Edge-based segmentation methods
		4.3 Threshold segmentation
	5. Conclusion
	References
Index