High Performance Computing for Intelligent Medical Systems

PRELIMS.pdf
	Preface
	Acknowledgements
	Editors biographies
		Varun Bajaj
		Irshad Ahmad Ansari
	Contributors biographies
		Ms Athena Abrishamchi
		Fatame Bafande
		Hussain Ahmed Choudhury
		Sengul Dogan
		Vandana Dubey
		Fatih Ertam
		Jamal Esmaelpoor
		Harsh Goud
		Kapil Gupta
		Lalita Gupta
		Smith K Khare
		Rajesh Kumar
		Wahengbam Kanan Kumar
		Gaurav Makwana
		Miguel Ángel Mañanas
		Hamid Reza Marateb
		Arezoo Mirshamsi
		Mohammad Reza Mohebbian
		Mohammad Hassan Moradi
		Kishorjit Nongmeikapam
		Saurabh Pal
		Antti Rissanen
		Marjo Rissanen
		Kalle Saastamoinen
		Zahra Momayez Sanat
		Prakash Chandra Sharma
		Mehdi Shirzadi
		Aheibam Dinamani Singh
		Mithlesh Prasad Singh
		Nidul Sinha
		Abdulhamit Subasi
		Turker Tuncer
		Amit Kumar Verma
		Dhyan Chandra Yadav
		Ram Narayan Yadav
		Shadi Zamani
CH001.pdf
	Chapter 1 Automatic detection of hypertension by flexible analytic wavelet transform using electrocardiogram signals
		1.1 Introduction
			1.1.1 Various intervals of ECG
			1.1.2 Related work
		1.2 Methodology
			1.2.1 Dataset
			1.2.2 Flexible analytic wavelet transform
			1.2.3 Feature extraction
			1.2.4 Classification techniques
			1.2.5 Performance parameters
		1.3 Results
		1.4 Conclusion
		References
CH002.pdf
	Chapter 2 Computational intelligence in surface electromyogram signal classification
		2.1 Introduction
		2.2 Computational intelligence in biomedical signal processing
		2.3 Background
			2.3.1 Discrete cosine transform
			2.3.2 Fast Fourier transform
			2.3.3 Singular value decomposition
			2.3.4 Ternary pattern
			2.3.5 Support vector machine
			2.3.6 Linear discriminant analysis
			2.3.7 KNN
			2.3.8 Artificial neural network
		2.4 Spider network
			2.4.1 Pre-processing
			2.4.2 Feature extraction
			2.4.3 Feature reduction
			2.4.4 Feature concatenation
			2.4.5 Classification
		2.5 Results and discussions
			2.5.1 Dataset
			2.5.2 Experimental results
			2.5.3 Discussion
		2.6 Conclusions and suggestions
		References
CH003.pdf
	Chapter 3 Analysis of IoT interventions to solve voice pathologies challenges
		3.1 Introduction
			3.1.1 Pathology assessment
			3.1.2 Internet of things in voice pathology
		3.2 Electroglottography
			3.2.1 Quantitative analysis
		3.3 Voice pathology datasets
			3.3.1 Voice ICar fEDerico II (VOICED)
			3.3.2 Massachusetts eye and ear infirmary
			3.3.3 Saarbruecken Voice Database
			3.3.4 Arabic voice pathology database
		3.4 Acoustic speech features with machine learning for voice pathology classification
			3.4.1 Feature extraction techniques
			3.4.2 Voice pathology analysis and detection techniques
		3.5 Discussion and conclusion
		References
CH004.pdf
	Chapter 4 Deep learning for cuffless blood pressure monitoring
		4.1 Introduction
		4.2 Physiological models
		4.3 Data source
			4.3.1 Preprocessing procedures
		4.4 Deep learning models for blood pressure monitoring
			4.4.1 LSTM model
			4.4.2 PCA-LSTM model
			4.4.3 Convolutional neural network model
			4.4.4 CNN–LSTM model
		4.5 Discussion
			4.5.1 Comparison with other methods
		4.6 Conclusion
		References
CH005.pdf
	Chapter 5 Reliability of machine learning methods for diagnosis and prognosis during the COVID-19 pandemic: a comprehensive critical review
		5.1 Introduction
		5.2 Methods
			5.2.1 January–March
			5.2.2 April–June
			5.2.3 July–September
			5.2.4 October 2020 to February 2021
			5.2.5 Machine learning methods
			5.2.6 Critical issues
		5.3 Conclusion and future scope
		References
CH006.pdf
	Chapter 6 Forecasting confirmed cases of Corona patients in India using regression and Gaussian analysis
		6.1 Introduction
		6.2 Regression analysis in machine learning
		6.3 Related work
		6.4 Methodology
			6.4.1 Data description
		6.5 Results
		6.6 Discussion
		6.7 Conclusion
		Acknowledgments
		References
CH007.pdf
	Chapter 7 A model for advanced patient feedback procedures in diagnostics
		7.1 Introduction
		7.2 Focus on diagnostics
			7.2.1 Diagnostic error as a concept
			7.2.2 Diagnostic errors in healthcare
			7.2.3 Common reasons for diagnostic failures
			7.2.4 Preventing diagnostic errors in cooperation with patients
		7.3 Diagnostics and safety challenges in healthcare
			7.3.1 Patient safety and equity challenges
			7.3.2 Enhanced patient safety with rational cost control policy
		7.4 Importance of patient feedback in the diagnostics phase
			7.4.1 Need for timely feedback
			7.4.2 The role of timely feedback
		7.5 The challenges of diagnostics-centered clients’ feedback
		7.6 Enhancing technology acceptance in system development
		7.7 Phases of diagnostics and the requirements for doctors
			7.7.1 Requirements for competence and compassion
			7.7.2 Diagnostic process from the view of doctors
			7.7.3 Diagnostic process from the view of patients
		7.8 A model for instant patient feedback
			7.8.1 General principles
			7.8.2 Structure of the model
			7.8.3 Patient management with the model
			7.8.4 Meaning of the fixed format phase of the model—phase 1
			7.8.5 Meaning and management of the free format phase—phase 2
			7.8.6 Clients’ opinions of the feedback delivery system—phase 3
		7.9 Client feedback as a translational development challenge
			7.9.1 Enhancing process synergy in organizations
			7.9.2 Maturing and validating patient-targeted feedback systems
		7.10 Conclusion
		References
CH008.pdf
	Chapter 8 Soft computing techniques for efficient processing of large medical data
		8.1 Introduction
		8.2 Understanding the concept: video compression
		8.3 Image compression standards
			8.3.1 JPEG
			8.3.2 JPEG2000
			8.3.3 JPEG-LS
			8.3.4 JPEG-XR
			8.3.5 H.265
			8.3.6 Types of coding and frames
		8.4 Motion estimation and the necessity of it in video coding?
			8.4.1 Forward and backward motion estimation
			8.4.2 Block matching concept
		8.5 What is soft computing: techniques and differences
		8.6 Standard techniques for motion estimation
		8.7 Soft computing techniques for motion estimation
		8.8 Conceptual terms used in different SC techniques
			8.8.1 Chromosomes and genes
			8.8.2 Chromosome representation
			8.8.3 Cross-over
			8.8.4 Mutation
			8.8.5 Weighting function and PBME
		8.9 Some well-established soft computing based BMA
			8.9.1 Genetic algorithm-BMA
			8.9.2 Inter-block/inter-frame fuzzy search algorithm
			8.9.3 Basic block-matching using particle swarm optimization
			8.9.4 Harmony search block matching algorithm
			8.9.5 Cat swarm optimization (CSO-BMA)
			8.9.6 CUCKOO search based BMA (CS-BMA)
			8.9.7 The ABC-BM algorithm
			8.9.8 ABC-DE
			8.9.9 HS-DE based BMA
			8.9.10 ‘Deterministically starting-GA’ (GADet)
			8.9.11 Enhanced Grey-wolf optimizer-BMA (EGWO-BMA)
			8.9.12 Chessboard search pattern strategy
		8.10 Results and discussion
		Acknowledgment
		References
CH009.pdf
	Chapter 9 A comparison of Parkinson’s disease prediction using ensemble data mining techniques with features selection methods
		9.1 Introduction
		9.2 Related work
		9.3 Methodology
			9.3.1 Data description
			9.3.2 Whisker plotting
			9.3.3 Histogram plotting
		9.4 Algorithms description
			9.4.1 Decision tree
			9.4.2 Naïve Bayes
			9.4.3 Random forest
			9.4.4 Extra tree
			9.4.5 Bagging ensemble method
			9.4.6 Features selection method in Parkinson’s disease
		9.5 Results
			9.5.1 Evaluation of result after prediction on Parkinson’s dataset
			9.5.2 Result of features importance methods
			9.5.3 Chi-square test
			9.5.4 Extra tree
			9.5.5 Heat map
			9.5.6 Evaluation of results after features selection
		9.6 Discussion
		9.7 Conclusion
		Acknowledgments
		References
CH010.pdf
	Chapter 10 A comparative analysis of image enhancement techniques for detection of microcalcification in screening mammogram
		10.1 Introduction
		10.2 Image enhancement in spatial domain
			10.2.1 Histogram modeling
			10.2.2 Histogram equalization
			10.2.3 Histogram matching
			10.2.4 Averaging filter
			10.2.5 Gaussian filter
			10.2.6 Median filter
		10.3 Image enhancement in frequency domain
			10.3.1 Butterworth filtering
			10.3.2 Gaussian low-pass filter
			10.3.3 Homomorphic filtering
			10.3.4 Discrete wavelet transform
		10.4 Convolutional neural network
		10.5 Evaluation criteria
			10.5.1 Mean square error
			10.5.2 Peak signal-to-noise ratio
			10.5.3 SNR
			10.5.4 Mean
			10.5.5 Variance
		10.6 Results and discussion
		10.7 Conclusion
		References
CH011.pdf
	Chapter 11 Computational intelligence for eye disease detection
		11.1 Introduction
		11.2 Anatomy of the eye
			11.2.1 The cornea
			11.2.2 The human retina
		11.3 Retinal diseases
			11.3.1 Retinal tear
			11.3.2 Diabetic retinopathy
			11.3.3 Macula hole
			11.3.4 Degeneration of the macula
			11.3.5 Disorders of the optic nerve
			11.3.6 Glaucoma
			11.3.7 Diabetic macular edema
			11.3.8 Retinopathy of prematurity
		11.4 History of retinal imaging
		11.5 Current status of retinal analysis
			11.5.1 Fundus imaging
			11.5.2 Optical coherence tomography
		11.6 Disease specific analysis of retinal images
			11.6.1 Early detection of retinal disease from fundus photography
			11.6.2 Early detection of systemic disease from fundus photography
			11.6.3 3-Dimensional OCT and retinal diseases—image guided therapy
		11.7 Fundus image analysis
			11.7.1 Glaucoma detection using retinal imaging
			11.7.2 Dementia detection using retinal imaging
			11.7.3 Heart diseases detection using retinal imaging
			11.7.4 Choroidal melanoma detection using retinal imaging
			11.7.5 Advantages of retinal imaging
		11.8 Comparative analysis between various retinal imaging methods
		11.9 Conclusion
		References
CH012.pdf
	Chapter 12 Recent trends in medical image segmentation with special focus on brain tumours and retinal images
		12.1 Introduction
			12.1.1 Types of biomedical imaging
			12.1.2 Biomedical image segmentation
			12.1.3 Segmentation evaluation
		12.2 Retinal images segmentation
			12.2.1 Datasets
			12.2.2 Fundus photography
			12.2.3 Challenges in retinal vessel segmentation
			12.2.4 Review of literature
		12.3 Brain tumour segmentation
			12.3.1 Brain tumour segmentation databases
			12.3.2 Classification of brain tumour
			12.3.3 Brain photography
			12.3.4 Challenges
			12.3.5 Image pre-processing
			12.3.6 Image post-processing
			12.3.7 Traditional machine learning in tumor segmentation
			12.3.8 Deep learning
		12.4 Discussion and conclusion
		References
CH013.pdf
	Chapter 13 Analysis of AI based PID controller for health care system
		13.1 Introduction
		13.2 Types of chronic disease
			13.2.1 Cancer
			13.2.2 Blood pressure
			13.2.3 Diabetes
			13.2.4 Arthritis
		13.3 Analysis of the biomedical system
			13.3.1 Classical controller in a biomedical system
		13.4 Mathematical modeling of the biomedical system
			13.4.1 MAP control
			13.4.2 Intracranial tumor’s temperature control
			13.4.3 Blood glucose
			13.4.4 BP control after surgery in a diabetic patient
			13.4.5 Heart modeling using PM
			13.4.6 Tumor growth control
		13.5 Conclusion
		References