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Table of Contents
Intro
Editor biographies
Navid Razmjooy
Venkatesan Rajinikanth
List of contributors
Outline placeholder
Ali Saud Al-Bimani
Noradin Ghadimi
Seifedine Kadry
Hong Lin
Suresh Manic
Rajesh Kannan
J Sivakumar
Uma Suresh
Chapter 1 Health informatics system
1.1 Introduction to health informatics
1.2 Traditional scheme
1.3 Recent advancements
1.4 Artificial intelligence schemes
1.5 Deep-learning schemes
1.6 The Internet of Medical Things in health informatics
1.7 Health-band-supported patient monitoring
1.8 Accurate disease diagnosis
1.9 Summary
References
Chapter 2 Medical-imaging-supported disease diagnosis
2.1 Introduction
2.2 Cancer prevention
2.3 Early detection
2.4 Internal organs and medical imaging
2.4.1 Lung abnormality examination
2.4.2 Colon/rectum abnormality examination
2.4.3 Liver abnormality examination
2.4.4 Breast abnormality examination
2.4.5 Skin cancer examination
2.4.6 Brain cancer examination
2.4.7 COVID-19 examination
2.5 Summary
References
Chapter 3 Traditional and AI-based data enhancement
3.1 Clinical image improvement practices
3.2 Significance of image enrichment
3.3 Common image improvement methods
3.3.1 Artifact elimination
3.3.2 Noise elimination
3.3.3 Contrast enhancement
3.3.4 Image edge detection
3.3.5 Restoration
3.3.6 Image smoothing
3.3.7 Saliency detection
3.3.8 Local binary pattern
3.3.9 Image thresholding
3.4 Summary
References
Chapter 4 Computer-aided-scheme for automatic classification of brain MRI slices into normal/Alzheimer's disease
4.1 Introduction
4.2 Related work
4.3 Methodology
4.3.1 Proposed AD detection scheme
4.3.2 Machine-learning scheme
4.3.3 Deep-learning scheme
4.3.4 Scheme with integrated features.
4.3.5 Data collection and pre-processing
4.3.6 Feature extraction and selection
4.3.7 Validation
4.4 Results and discussions
4.5 Conclusion
Conflict of interest
References
Chapter 5 Design of a system for melanoma diagnosis using image processing and hybrid optimization techniques
5.1 Introduction
5.1.1 Conception
5.2 Literature review
5.3 Materials and methods
5.3.1 Artificial neural networks
5.3.2 Concept
5.3.3 Mathematical modeling of an ANN
5.4 Meta-heuristics
5.5 Electromagnetic field optimization algorithm
5.6 Developed electromagnetic field optimization algorithm
5.7 Simulation results
5.7.1 Image acquisition
5.7.2 Pre-processing stage
5.7.3 Processing stage
5.7.4 Classification
5.8 Final evaluation
5.9 Conclusions
References
Chapter 6 Evaluation of COVID-19 lesion from CT scan slices: a study using entropy-based thresholding and DRLS segmentation
6.1 Introduction
6.2 Context
6.3 Methodology
6.3.1 COVID-19 database
6.3.2 Image conversion and pre-processing
6.3.3 Image thresholding
6.3.4 Distance regularized level set segmentation
6.3.5 Performance computation and validation
6.4 Results and discussions
6.5 Conclusion
References
Chapter 7 Automated classification of brain tumors into LGG/HGG using concatenated deep and handcrafted features
7.1 Introduction
7.2 Context
7.3 Methodology
7.3.1 Image databases
7.3.2 Handcrafted feature extraction
7.3.3 Deep feature extraction
7.3.4 Feature concatenation
7.3.5 Performance measure computation and validation
7.4 Results and discussion
7.5 Conclusion
References
Chapter 8 Detection of brain tumors in MRI slices using traditional features with AI scheme: a study
8.1 Introduction
8.2 Context
8.3 Methodology
8.3.1 Image data sets.
8.3.2 Pre-processing
8.3.3 Post-processing
8.3.4 Feature extraction
8.3.5 Classification
8.3.6 Performance evaluation
8.4 Results and discussion
8.5 Conclusion
Acknowledgment
References
Chapter 9 Framework to classify EEG signals into normal/schizophrenic classes with machine-learning scheme
9.1 Introduction
9.2 Related work
9.3 Methodology
9.3.1 Electroencephalogram database
9.3.2 EEG pre-processing
9.3.3 Feature selection
9.3.4 Classification
9.3.5 Validation
9.4 Results and discussion
9.5 Conclusion
References
Chapter 10 Computerized classification of multichannel EEG signals into normal/autistic classes using image-to-signal transformation
10.1 Introduction
10.2 Context
10.3 Problem formulation
10.4 Methodology
10.4.1 Electroencephalogram database
10.4.2 Signal-to-image conversion with continuous wavelet transform
10.4.3 Nonlinear feature extraction
10.4.4 Locality-sensitive discriminant-analysis-based data reduction
10.4.5 Classifier implementation
10.4.6 Performance measure and validation
10.5 Results and discussion
10.6 Conclusion
References.
Editor biographies
Navid Razmjooy
Venkatesan Rajinikanth
List of contributors
Outline placeholder
Ali Saud Al-Bimani
Noradin Ghadimi
Seifedine Kadry
Hong Lin
Suresh Manic
Rajesh Kannan
J Sivakumar
Uma Suresh
Chapter 1 Health informatics system
1.1 Introduction to health informatics
1.2 Traditional scheme
1.3 Recent advancements
1.4 Artificial intelligence schemes
1.5 Deep-learning schemes
1.6 The Internet of Medical Things in health informatics
1.7 Health-band-supported patient monitoring
1.8 Accurate disease diagnosis
1.9 Summary
References
Chapter 2 Medical-imaging-supported disease diagnosis
2.1 Introduction
2.2 Cancer prevention
2.3 Early detection
2.4 Internal organs and medical imaging
2.4.1 Lung abnormality examination
2.4.2 Colon/rectum abnormality examination
2.4.3 Liver abnormality examination
2.4.4 Breast abnormality examination
2.4.5 Skin cancer examination
2.4.6 Brain cancer examination
2.4.7 COVID-19 examination
2.5 Summary
References
Chapter 3 Traditional and AI-based data enhancement
3.1 Clinical image improvement practices
3.2 Significance of image enrichment
3.3 Common image improvement methods
3.3.1 Artifact elimination
3.3.2 Noise elimination
3.3.3 Contrast enhancement
3.3.4 Image edge detection
3.3.5 Restoration
3.3.6 Image smoothing
3.3.7 Saliency detection
3.3.8 Local binary pattern
3.3.9 Image thresholding
3.4 Summary
References
Chapter 4 Computer-aided-scheme for automatic classification of brain MRI slices into normal/Alzheimer's disease
4.1 Introduction
4.2 Related work
4.3 Methodology
4.3.1 Proposed AD detection scheme
4.3.2 Machine-learning scheme
4.3.3 Deep-learning scheme
4.3.4 Scheme with integrated features.
4.3.5 Data collection and pre-processing
4.3.6 Feature extraction and selection
4.3.7 Validation
4.4 Results and discussions
4.5 Conclusion
Conflict of interest
References
Chapter 5 Design of a system for melanoma diagnosis using image processing and hybrid optimization techniques
5.1 Introduction
5.1.1 Conception
5.2 Literature review
5.3 Materials and methods
5.3.1 Artificial neural networks
5.3.2 Concept
5.3.3 Mathematical modeling of an ANN
5.4 Meta-heuristics
5.5 Electromagnetic field optimization algorithm
5.6 Developed electromagnetic field optimization algorithm
5.7 Simulation results
5.7.1 Image acquisition
5.7.2 Pre-processing stage
5.7.3 Processing stage
5.7.4 Classification
5.8 Final evaluation
5.9 Conclusions
References
Chapter 6 Evaluation of COVID-19 lesion from CT scan slices: a study using entropy-based thresholding and DRLS segmentation
6.1 Introduction
6.2 Context
6.3 Methodology
6.3.1 COVID-19 database
6.3.2 Image conversion and pre-processing
6.3.3 Image thresholding
6.3.4 Distance regularized level set segmentation
6.3.5 Performance computation and validation
6.4 Results and discussions
6.5 Conclusion
References
Chapter 7 Automated classification of brain tumors into LGG/HGG using concatenated deep and handcrafted features
7.1 Introduction
7.2 Context
7.3 Methodology
7.3.1 Image databases
7.3.2 Handcrafted feature extraction
7.3.3 Deep feature extraction
7.3.4 Feature concatenation
7.3.5 Performance measure computation and validation
7.4 Results and discussion
7.5 Conclusion
References
Chapter 8 Detection of brain tumors in MRI slices using traditional features with AI scheme: a study
8.1 Introduction
8.2 Context
8.3 Methodology
8.3.1 Image data sets.
8.3.2 Pre-processing
8.3.3 Post-processing
8.3.4 Feature extraction
8.3.5 Classification
8.3.6 Performance evaluation
8.4 Results and discussion
8.5 Conclusion
Acknowledgment
References
Chapter 9 Framework to classify EEG signals into normal/schizophrenic classes with machine-learning scheme
9.1 Introduction
9.2 Related work
9.3 Methodology
9.3.1 Electroencephalogram database
9.3.2 EEG pre-processing
9.3.3 Feature selection
9.3.4 Classification
9.3.5 Validation
9.4 Results and discussion
9.5 Conclusion
References
Chapter 10 Computerized classification of multichannel EEG signals into normal/autistic classes using image-to-signal transformation
10.1 Introduction
10.2 Context
10.3 Problem formulation
10.4 Methodology
10.4.1 Electroencephalogram database
10.4.2 Signal-to-image conversion with continuous wavelet transform
10.4.3 Nonlinear feature extraction
10.4.4 Locality-sensitive discriminant-analysis-based data reduction
10.4.5 Classifier implementation
10.4.6 Performance measure and validation
10.5 Results and discussion
10.6 Conclusion
References.