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.