Medical Informatics and Bioimaging Using Artificial Intelligence: Challenges, Issues, Innovations and Recent Developments (Studies in Computational Intelligence, 1005)

✍ Scribed by Aboul Ella Hassanien (editor), Roheet Bhatnagar (editor), Václav Snášel (editor), Mahmoud Yasin Shams (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 256
• Category: Library

✦ Synopsis

This book emphasizes the latest developments and achievements in artificial intelligence and related technologies, focusing on the applications of artificial intelligence and medical diagnosis. The book describes the theory, applications, concept visualization, and critical surveys covering most aspects of AI for medical informatics.

✦ Table of Contents

Preface
Contents
Machine Learning and Deep Learning Applications in Medical Diagnosis
A Deep Learning Based Cockroach Swarm Optimization Approach for Segmenting Brain MRI Images
1 Introduction
2 Preliminaries
2.1 Cockroach Swarm Optimization
2.2 Deep Learning
3 The Proposed Approach
4 Experimental Results and Discussion
5 Conclusion and Future Directions
References
Efficient Classification Model for Melanoma Based on Convolutional Neural Networks
1 Introduction
2 Related Work
3 Materials and Methods
3.1 Imbalanced Data Handling
3.2 Deep Learning Approach
4 The Proposed Melanoma Classification Model
4.1 Description of Dataset
4.2 Data Preprocessing
4.3 Data Preprocessing
5 Experimental Results
6 Conclusion
References
Machine Learning-Supported MRI Analysis of Brain Asymmetry for Early Diagnosis of Dementia
1 Introduction
2 Literature Review
2.1 Brain Asymmetry for the Diagnosis of Brain-Related Disorders
2.2 Classification of Alzheimer’s Disease and Early Mild Cognitive Impairment Using the ADNI Database
3 The Proposed Approach
3.1 Image Preprocessing and Segmentation of Image Asymmetry
3.2 Feature Engineering and Analysis
4 Experiments and Results
5 Discussion and Conclusion
References
Feature Selection Based Coral Reefs Optimization for Breast Cancer Classification
1 Introduction
2 Related Work
3 Methods and Materials
3.1 The CRO Algorithm (Coral Reef Optimization)
3.2 CRO as a Feature Selection Technique
3.3 Utilized Classification Algorithms
3.4 Proposed Method
4 Results and Discussion
4.1 Dataset Description
4.2 Experiments and Results
4.3 Statistical Results Analysis
5 Conclusion and Future Work
References
A Comprehensive Review on Brain Disease Mapping—The Underlying Technologies and AI Based Techniques for Feature Extraction and Classification Using EEG Signals
1 Introduction
2 Raw EEG Signals
3 Signal Processing
4 Feature Extraction Method
5 Classification
6 Case Studies Consolidation and Their Analysis
7 Conclusion
References
Recognition of Ocular Disease Based Optimized VGG-Net Models
1 Introduction
2 Related Work
3 Methodologies
3.1 The Proposed Model
3.2 Ocular Disease Recognition Dataset
3.3 DCNN Classification Models
4 Experimental Results and Discussion
4.1 Performance Evaluation
4.2 Confusion Matrix
5 Conclusion
References
Artificial Intelligence in COVID—19 Diagnosis
Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life
1 Introduction
2 Applications of AI and IoT in COVID-19
2.1 Diagnosis of COVID-19
2.2 Drug and Vaccine Development
2.3 Distribution of COVID-19 Vaccine Using AI
2.4 Monitoring Vaccine Temperate Using IoT
2.5 Management of Vaccine (Co-WIN)
2.6 Application of AI in Predicting COVID-19 Outcome
2.7 Sentiment Analysis and Fake News Detection Regarding Covid-19
3 Impact of COVID-19 Vaccine on Social Life
4 The Plans of Government and Hospitals to Reduce the Impact of Corona Especially in Saudi Arabia
5 Acceptance and Demand for COVID-19 Vaccines
6 Conclusion
References
COVID-19 Forecasting Based on an Improved Interior Search Algorithm and Multilayer Feed-Forward Neural Network
1 Introduction
2 Preliminaries
2.1 Multilayer Feed-Forward Neural Network
2.2 Interior Search Algorithm
2.3 The Motivation of This Work
3 Proposed ISACL Algorithm
3.1 Composition-Based Experience Strategy
3.2 Chaotic Learning Based-Local Searching Mechanism
3.3 Framework of ISACL
4 Experiment and Results
4.1 Description of the COVID-19 Dataset
4.2 Parameter Settings
4.3 Indices for Performance Assessment
4.4 Results and Discussions
5 Conclusions
References
Development of Disease Diagnosis Model for CXR Images and Reports—A Deep Learning Approach
1 Introduction
2 Background Study
2.1 CXR Images and Radiology Reports
2.2 Convolutional Neural Networks (CNNs)
2.3 Pre-trained Convolutional Neural Networks (CNNs)
2.4 Radiology Report
2.5 Natural Language Processing in Producing the Radiology Report
3 Literature Review
4 Methodology
4.1 Dataset Description
4.2 Exploratory Data Analysis
4.3 Building Models
5 Model Evaluation and Results Analysis
6 Conclusion
References
Intelligent Drug Descriptors Analysis: Toward COVID-19 Drug Repurposing
1 Introduction
2 Discussion
2.1 Physicochemical Properties and Descriptors
2.2 Drug Approved Analysis
3 Theoretical Background of Support Vector Machine (SVM)
4 Intelligent Drugs Descriptors Analysis Approach
4.1 Preprocessing Phase
4.2 Support Vector Regression Phase
4.3 Kernel Selection
4.4 Descriptors Analysis
5 Results and Discussion
6 Conclusion and Future Work
7 Appendix
References
Emerging Technologies and Applications
Analysis of Aortic Valve Using a Finite Element Model
1 Introduction
2 Materials and Methods
3 Results
3.1 Aortic Root and Leaflet Deformation
3.2 Von-Mises Stress in the Leaflets
4 Conclusion and Future Work
References
Fundus Images Enhancement Using Gravitational Force and Lateral Inhibition Network for Blood Vessel Detection
1 Introduction
2 Material and Methods
2.1 Description of Dataset
2.2 Background of Model
3 GF and LIN Pattern to Improve the Image Contrast of a Vessel Image
4 Metrics for Evaluating the Improved Image
5 Results and Discussion
6 Conclusion
References
Multivariate Fuzzy Logic Based Smart Healthcare Monitoring for Risk Evaluation of Cardiac Patients
1 Introduction
2 Related Work
3 General Layout of Fuzzy Logic Theory
4 Proposed Fuzzy-Based Cardiac Patient Monitoring Model
4.1 Simulation Environment Setting
5 Simulation Results and Analysis
6 Conclusions
References
Ensemble Machine Learning Model for Mortality Prediction Inside Intensive Care Unit
1 Introduction
2 Related Work
2.1 Scoring System
2.2 Machine Learning Systems
3 Data Set
3.1 Dataset Description
3.2 Table Selection
4 Proposed Framework
4.1 Data Preprocessing
5 Resuls and Discussion
5.1 Single Classifier Results
5.2 Ensemble Classifier Results
6 Conclusion and Future Work
References