High Performance Computing for Intelligent Medical Systems
- Length: 322 pages
- Edition: 1
- Language: English
- Publisher: Iop Publishing Ltd
- Publication Date: 2021-11-30
- ISBN-10: 075033813X
- ISBN-13: 9780750338134
- Sales Rank: #0 (See Top 100 Books)
Modern medicine and healthcare are highly dependent on engineering, employing instrumentation and computer systems to aid investigation, diagnosis, treatment and patient management. The significant developments in the field of computational intelligence, combined with the emergence of high-performance computing is impacting society in many ways, and the health sector is no exception. The interface of high-performance computing, computational intelligence and medical science, has seen the emergence of intelligent medical systems. These systems can provide a deeper insight into many healthcare and medical problems. It can also aid in controlling, analyzing and the management of medical applications and can provide significant improvement in the quality of life and efficacy of clinical treatment. However, the successful application of high-performance computing in medicine requires in-depth knowledge and understanding of medical systems.
This book focuses on the advances and applications of high-performance computing for medical systems and provides an insight into the latest developments in the field. It will help readers to understand the high-performance computing research domain as related to intelligent medical systems, its effect on our lives and its present limitations.
Key features
- Incorporates a range of cutting-edge computing technologies as applied to healthcare.
- International expert authors.
- Reviews present progress and future challenges
- Focus on applications.
- Interdisciplinary readership.
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 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 7.8.5 Meaning and management of the free format phase—phase 7.8.6 Clients’ opinions of the feedback delivery system—phase 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
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