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The book analyzes the combination of intelligent data analytics with the intricacies of biological data that has become a crucial factor for innovation and growth in the fast-changing field of bioinformatics and biomedical systems.
Intelligent Data Analytics for Bioinformatics and Biomedical Systems delves into the transformative nature of data analytics for bioinformatics and biomedical research. It offers a thorough examination of advanced techniques, methodologies, and applications that utilize intelligence to improve results in the healthcare sector. With the exponential growth of data in these domains, the book explores how computational intelligence and advanced analytic techniques can be harnessed to extract insights, drive informed decisions, and unlock hidden patterns from vast datasets. From genomic analysis to disease diagnostics and personalized medicine, the book aims to showcase intelligent approaches that enable researchers, clinicians, and data scientists to unravel complex biological processes and make significant strides in understanding human health and diseases.
This book is divided into three sections, each focusing on computational intelligence and data sets in biomedical systems. The first section discusses the fundamental concepts of computational intelligence and big data in the context of bioinformatics. This section emphasizes data mining, pattern recognition, and knowledge discovery for bioinformatics applications. The second part talks about computational intelligence and big data in biomedical systems. Based on how these advanced techniques are utilized in the system, this section discusses how personalized medicine and precision healthcare enable treatment based on individual data and genetic profiles. The last section investigates the challenges and future directions of computational intelligence and big data in bioinformatics and biomedical systems. This section concludes with discussions on the potential impact of computational intelligence on addressing global healthcare challenges.
Audience
Intelligent Data Analytics for Bioinformatics and Biomedical Systems is primarily targeted to professionals and researchers in bioinformatics, genetics, molecular biology, biomedical engineering, and healthcare. The book will also suit academicians, students, and professionals working in pharmaceuticals and interpreting biomedical data.
Neha Sharma PhD, is an assistant professor in the Department of Computer Science and Engineering, Chitkara University, Rajpura, India. She has more than 60 international publications in reputed peer-reviewed journals. She has also published more than 30 national & international patents under the Intellectual Property Rights of the governments of India and abroad. Her main areas of research are in image processing, machine learning, deep learning, and cybersecurity.
Korhan Cengiz, PhD, is an assistant professor at the Department of Information Technologies, Faculty of Informatics and Management, University of Hradec Kralove, Kralove, Czech Republic. He obtained his doctorate in electronics engineering from Kadir Has University, Istanbul, Turkey, in 2016 and has authored more than 40 SCI articles, five international patents, ten chapters in books, and one book. His research interests include wireless sensor networks, wireless communications, statistical signal processing, etc.
Prasenjit Chatterjee, PhD, is a professor of mechanical engineering and dean (research and consultancy) at MCKV Institute of Engineering, West Bengal, India. He has authored several books on intelligent decision-making, fuzzy computing, supply chain management, etc. He has over 6850 citations and many research papers in various international journals. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) and Ranking Alternatives through Functional Mapping of Criterion Sub-Intervals into a Single Interval (RAFSI).
Preface xix
Acknowledgment xxv
1 Advancements in Machine Learning Techniques for Biological Data Analysis 1 S. Kanakaprabha, G. Ganesh Kumar, Y. Padma, Gangavarapu and Venkata Nagaraju Thatha
1.1 Introduction 1
1.1.1 Significance of Advanced Data Analysis in Biology 2
1.2 Literature Survey 3
1.3 Machine Learning Fundamentals 5
1.3.1 Supervised, Unsupervised, and Semi-Supervised Learning 6
1.3.2 Feature Engineering and Selection 6
1.3.3 Deep Learning Architectures for Biological Data 7
1.4 Genomic Sequence Analysis 7
1.4.1 DNA Sequence Classification and Prediction 8
1.4.2 Genomic Variant Analysis with Machine Learning 8
1.4.3 Enhancing Epigenetic Studies through AI 8
1.5 Proteomic Profiling and Structural Prediction 9
1.5.1 Protein Structure Prediction Using Deep Learning 10
1.5.2 Peptide and Protein Identification via Machine Learning 11
1.5.3 Functional Annotation of Proteins 11
1.6 Metabolomics and Pathway Analysis 12
1.6.1 Metabolite Identification and Quantification 14
1.6.2 Metabolic Pathway Reconstruction Using AI 14
1.6.3 Integrative Analysis of Multi-Omics Data 15
1.7 Medical Applications 15
1.7.1 Disease Diagnosis and Biomarker Discovery 15
1.7.2 Personalized Treatment and Drug Discovery 16
1.7.3 Predictive Modeling for Clinical Outcomes 16
1.7.4 Drug Repurposing and Adverse Event Prediction 17
1.7.5 Neuroinformatics and Brain Disorders 17
1.8 Challenges and Future Directions 17
1.8.1 Interpretable Machine Learning in Biology 21
1.8.2 Addressing Data Privacy and Ethics 21
1.8.3 Advancing Quantum Computing in Biological Data Analysis 22
1.8.4 Handling Heterogeneous and Multi-Modal Data 22
1.8.5 Small Data and Imbalanced Datasets 22
1.8.6 Clinical Adoption and Validation 22
1.8.7 Ethical and Societal Implications 23
1.9 Conclusion 23
1.9.1 Synthesis of Key Contributions and Insights 23
1.9.2 Anticipated Transformations in Biological Research 24
References 24
2 Predictive Analytics in Medical Diagnosis 27 Vivek Upadhyaya
2.1 Introduction to Predictive Analytics in Healthcare 28
2.1.1 Definition of Predictive Analytics 28
2.1.2 The Significance of Predictive Analytics in Medical Diagnosis 29
2.2 Overview of the Chapter's Structure 29
2.3 Data Sources and Data Preprocessing 30
2.3.1 Types of Data Sources (Electronic Health Records, Wearable Devices, Genetic Data, etc.) 31
2.4 Data Quality and Cleaning 33
2.4.1 Feature Selection and Engineering 33
2.4.2 Dealing with Missing Data 35
2.5 Predictive Analytics Techniques 36
2.5.1 Regression Analysis 36
2.5.2 Classification Models (e.g., Logistic Regression, Decision Trees, Random Forests) 37
2.5.3 Machine Learning Algorithms (e.g., Support Vector Machines, Neural Networks) 39
2.5.4 Time Series Analysis 40
2.6 Use Cases in Medical Diagnosis 40
2.6.1 Early Detection of Diseases (e.g., Cancer, Diabetes) 42
2.6.2 Risk Assessment and Stratification 42
2.6.3 Personalized Treatment Recommendations 43
2.6.4 Image Analysis and Medical Imaging 43
2.6.5 Disease Progression Tracking 46
2.6.6 Model Interpretability and Explainability 47
2.6.7 The Importance of Model Interpretability in Healthcare 47
2.6.8 Techniques for Making Predictive Models More Interpretable 48
2.6.9 Regulatory Considerations (e.g., GDPR, HIPAA) 49
2.6.10 Ethical and Legal Considerations 50
2.7 Challenges and Limitations 51
2.7.1 Data-Related Challenges (Data Volume, Quality, Interoperability) 53
2.7.2 Overfitting and Model Generalization 53
2.7.3 Addressing Bias and Fairness in Predictive Models 54
2.7.4 Successful Implementation and Case Studies 55
2.7.5 Real-World Examples of Healthcare Institutions Successfully Using Predictive Analytics 56
2.8 Future Trends and Innovations 58
2.8.1 The Role of Artificial Intelligence and Deep Learning 59
2.8.2 Integration with Electronic Health Records and Telemedicine 60
2.8.3 The Potential Impact of Quantum Computing on Medical Diagnosis 60
2.9 Conclusion 62
References 63
3 Skin Disease Detection and Classification 67 M. Aamir Gulzar, Salman Iqbal, Akhtar Jamil, Alaa Ali Hameed and Faezeh Soleimani
3.1 Introduction 68
3.2 Related Work 69
3.3 Data 70
3.4 Methodology 71
3.4.1 Data Pre-Processing 71
3.4.2 Image Enhancement 72
3.4.3 Feature Extraction 73
3.4.4 Machine Learning Algorithm Used 74
3.5 Results 81
3.5.1 Experimental Setup 81
3.5.2 Data Preprocessing, Feature Extraction, and Model Selection 83
3.5.3 Evaluation Metrics 85
3.5.4 Classification and Outcomes 86
3.6 Conclusion 89
3.7 Future Work 90
References 91
4 Computer-Aided Polyp Detection Using Customized Convolutional Neural Network Architecture 93 Palak Handa, Nidhi Goel, S. Indu and Deepak Gunjan
4.1 Introduction 94
4.2 Related Works 96
4.3 Materials and Methods 96
4.3.1 Description of the Used Datasets and Their Preparation 96
4.3.2 Data Augmentation 96
4.3.3 Customized CNN 97
4.4 Results and Discussion 98
4.4.1 CNN Optimizers 99
4.4.2 Kernel Initializers 99
4.4.3 Color Space 100
4.4.4 Image Dimension 101
4.4.5 Kernel Size 101
4.4.6 Sample Maps of the CNN Features 103
4.4.7 Ablation Study 104
4.4.8 Comparison of the Proposed Architecture with Existing Deep-Learning Algorithms in This Field 104
4.5 Conclusion and Future Scope 105
References 106
5 Computational Intelligence Induced Risk in Modern Healthcare: Classical Review and Current Status 109 Nitish Ojha and Shrikant Ojha
5.1 Introduction 110
5.2 People-Based Risk 113
5.3 Doctor-Induced Risk 116
5.4 Patient-Based Risk 120
5.5 Process-Based Risk 121
5.6 Technology-Based Risk 129
5.7 Conclusion 138
References 139
6 A Hybrid Deep Learning Framework to Diagnose Sleep Apnea Using Electrocardiogram Signals for Smart Healthcare 145 Sampoorna Poria, Ahona Ghosh, Biswarup Ganguly and Sriparna Saha
6.1 Introduction 146
6.2 Proposed Methodology 148
6.2.1 Introduction to the Data Acquisition Device 148
6.2.2 Preprocessing Using Discrete Wavelet Transform 148
6.2.3 Feature Extraction Using Auto Encoder 149
6.2.4 Classification Using Bidirectional LSTM 150
6.3 Experiment Results and Discussions 152
6.3.1 Dataset Details 152
6.3.1.1 Preprocessing Outcomes 153
6.3.2 Feature Extraction Outcomes 154
6.3.3 Classification Results 155
6.3.4 Statistical Validation 156
6.3.5 Experimental Setup for Computer Aided Diagnosis System 158
6.3.6 Performance Evaluation 158
6.4 Conclusion and Future Scope 160
Acknowledgments 160
References 160
7 Deep Ensemble Feature Extraction Based Classification of Bleeding Regions Using Wireless Capsule Endoscopy Images 163 Srijita Bandopadhyay, Kyamelia Roy, Sheli Sinha Chaudhuri, Soumen Banerjee and Korhan Cengiz
7.1 Introduction 164
7.2 Related Works 164
7.3 Methodology 166
7.3.1 Dataset 167
7.3.2 Image Processing 168
7.3.3 Histogram Equalizer 169
7.3.4 Denoising 172
7.3.5 Adaptive Filtering 173
7.3.6 Augmentation 173
7.3.7 Data Processing 175
7.3.8 Convolutional Neural Network 175
7.3.8.1 ResNet 50 175
7.3.8.2 Vgg 16 176
7.3.8.3 Inception V 3 177
7.3.9 Feature Extraction 177
7.3.10 Feature Reconstruction 178
7.3.11 Classification 179
7.4 Results and Discussion 180
7.5 Conclusion 189
References 189
8 Advances in Brain Tumor Detection and Localization: A Comprehensive Survey 195 Krishnangshu Paul, Arunima Patra and Prithwineel Paul
8.1 Introduction 195
8.2 Background Study on Various Methods 198
8.2.1 Svm 198
8.2.1.1 Advantages 198
8.2.1.2 Limitations 199
8.2.2 Knn 199
8.2.2.1 Advantages 199
8.2.2.2 Limitations 199
8.2.3 Logistic Regression 200
8.2.3.1 Advantages 200
8.2.3.2 Limitations 200
8.2.4 Cnn 200
8.2.4.1 Advantages 201
8.2.4.2 Limitations 201
8.3 Methodology 202
8.4 Experimentation 205
8.4.1 Dataset 205
8.4.2 Results Achieved 206
8.5 Discussion 210
8.6 Conclusion 210
8.6.1 Future Scope 210
References 211
9 Integrating Apriori Algorithm with Data Mining Classification Techniques for Enhanced Primary Tumor Prediction 213 Khalid Mahboob, Nida Khalil, Fatima Waseem and Abeer Javed Syed
9.1 Overview 214
9.1.1 Feature Selection 216
9.1.2 Hyperparameter Tuning 216
9.1.3 Enhanced Primary Tumor Prediction 217
9.1.4 Continuous Improvement 217
9.1.5 Clinical Integration 217
9.2 Previous Studies on Tumor Prediction Using Data Mining and Apriori Algorithm 218
9.3 Data Mining Process 220
9.3.1 Data Collection and Pre-Processing 221
9.3.1.1 Data Cleaning 221
9.3.1.2 Data Transformation 221
9.3.1.3 Data Reduction 221
9.3.1.4 Data Integration 222
9.3.1.5 Data Discretization 222
9.3.2 Model(s) Selection and Building 222
9.3.2.1 Supervised Learning 222
9.3.2.2 Unsupervised Learning 223
9.3.2.3 Reinforcement Learning 223
9.3.2.4 Ensemble Method 224
9.3.3 Evaluation and Exploratory Data Analysis 224
9.3.3.1 Evaluation Techniques in Data Mining 225
9.4 Data Mining in Bioinformatics 225
9.5 Cancer and Tumor Biology 226
9.6 Data Mining Classification Techniques 228
9.6.1 J48 Decision Tree 229
9.6.2 Naïve Bayes 229
9.6.3 K-Nearest Neighbor 229
9.7 Apriori Algorithm and Association Rule Mining 230
9.8 Conclusion and Future Work 230
References 231
10 Deep Learning in Genomics, Personalized Medicine, and Neurodevelopmental Disorders 235 Ajay Sharma, Shashi Kala, Aman Kumar, Shamneesh Sharma, Gaurav Gupta and Varun Jaiswal
10.1 Introduction 236
10.1.1 Genomics, Genetics, and Personalized-Medicine Genetics 238
10.1.2 The "Omics" Revolution a Bioinformatics Perspective 239
10.2 Machine Learning in Personalized Medicine and Neurogenerative Disorder 241
10.2.1 Machine Learning Using Artificial Deep Neural Networks (DNN) 243
10.2.2 Limitations and Advantages of ML Over Traditional Approaches 245
10.3 Machine Learning in Genomics 246
10.3.1 Multi-Model Data Integration Using Machine Learning 249
10.4 Machine Learning and the Future of Medicine in Healthcare 251
10.4.1 Ethical and Legal Considerations of Precision Medicine 252
10.5 Genomics Technology and Application 255
10.5.1 High-Throughput DNA Sequencing Technology 255
10.5.2 Pharmacogenomics (PGx) 256
10.5.3 The Study of Drug Action is Divided into Different Categories: Pharmacokinetics and Pharmacodynamics 257
10.5.4 Circulating Cell-Free Nucleic Acids 257
10.5.5 Circulating Tumor Cells (CTCs) 258
10.5.6 Mitochondrial DNA (mtDNA) 258
10.6 Artificial Intelligence and Neurodegenerative Disorders 259
10.7 Conclusion 261
Conflict of Interest 261
Acknowledgments 262
References 262
11 Emerging Trends of Big Data in Bioinformatics and Challenges 265 Ajay Sharma, Tarun Pal, Utkarsha Naithani, Gaurav Gupta and Varun Jaiswal
11.1 Introduction 266
11.2 Human Genome 267
11.3 Next-Generation Sequencing 268
11.3.1 Challenges of NGS in Big Data 271
11.4 Bioinformatics Big Data Architecture 272
11.5 Big Data in Immunology 273
11.6 Structural Biology 275
11.7 Computer Science 277
11.8 Healthcare 280
11.8.1 Application of Big Data in Healthcare 282
11.9 Big Data Formats 282
11.9.1 Quantum Computing 284
11.10 Conclusion 285
Conflict of Interest 285
Acknowledgments 285
References 286
12 Wearable Devices and Health Monitoring: Big Data and AI for Remote Patient Care 291 S. Kanakaprabha, G. Ganesh Kumar, Bhargavi Peddi Reddy, Yallapragada Ravi Raju and P. Chandra Mohan Rai
12.1 Introduction 292
12.1.1 Importance of Remote Patient Monitoring 293
12.1.2 Significance of Big Data and AI in Healthcare 294
12.2 Related Work 294
12.3 Wearable Technologies in Healthcare 297
12.3.1 Types of Wearable Devices (Smartwatches, Fitness Trackers, Medical-Grade Wearables, etc.) 297
12.3.2 Applications in Monitoring Vital Signs (Heart Rate, Blood Pressure, Temperature, etc.) 298
12.3.3 Wearables for Tracking Physical Activity and Sleep Patterns 299
12.4 Remote Patient Monitoring 299
12.4.1 Definition and Benefits of Remote Patient Monitoring 300
12.5 Use Cases: Chronic Disease Management, Post-Operative Care, Elderly Care, Etc. 301
12.6 Challenges of Traditional In-Person Care vs. Remote Monitoring 302
12.7 Data Collection and Transmission 303
12.7.1 Sensors and Data Collection Methods in Wearables 303
12.8 Wireless Data Transmission Technologies (Bluetooth, Wi-Fi, Cellular, Etc.) 304
12.8.1 Ensuring Data Security and Privacy 304
12.8.2 Big-Data Analytics in Healthcare 304
12.8.3 Role of Big Data in Healthcare Decision-Making 305
12.8.4 Handling and Processing Large Volumes of Wearable-Generated Data 305
12.8.5 Data Storage, Integration, and Interoperability 305
12.8.6 AI and Machine Learning in Health Monitoring 306
12.9 Introduction to AI and ML Applications in Healthcare 306
12.9.1 Predictive Analytics for Early Disease Detection 307
12.9.2 Real-Time Anomaly Detection and Alerts 307
12.9.3 Clinical Decision Support Systems 307
12.9.4 Integration of AI Insights into Clinical Workflows 308
12.9.5 Enabling Personalized Treatment Plans Based on Wearable Data 308
12.9.6 Enhancing Healthcare Professional Decision-Making 308
12.9.7 Challenges and Ethical Considerations in Using Patient-Generated Data 309
12.10 Future Directions and Trends 309
12.11 Conclusion 310
References 311
13 Disease Biomarker Discovery with Big Data Analysis 313 G. Venu Gopal, Kanakaprabha S., Gangavarapu Moahana Rao, Yallapragada Ravi Raju and G. Ganesh Kumar
13.1 Introduction 314
13.1.1 The Need for Multi-Omics Data Integration in Biomarker Discovery 314
13.1.2 Role of Machine Learning in Multi-Omics Data Analysis 314
13.2 Literature Survey 316
13.3 Challenges in Multi-Omics Data Integration 319
13.3.1 Data Heterogeneity and Integration Challenges 319
13.3.2 Dimensionality Reduction and Feature Selection 319
13.3.3 Feature Representation and Integration Techniques 319
13.3.4 Early Fusion vs. Late Fusion Approaches 320
13.3.5 Network-Based Integration Methods 320
13.4 Deep Learning Architectures for Multi-Omics Data 320
13.4.1 Disease Subtyping and Stratification 321
13.4.2 Identification of Key Regulatory Pathways 322
13.4.3 Predictive Modeling for Treatment Response 322
13.4.4 Cancer Biomarker Discovery Using Multi-Omics Data 322
13.4.5 Neurological Disorder Classification through Integration 322
13.5 Evaluation Metrics and Validation Strategies 323
13.5.1 Cross-Validation Techniques for Multi-Omics Data 324
13.5.2 Assessing Robustness and Generalizability of Biomarker Models 325
13.6 Ethical Considerations in Biomarker Discovery 325
13.6.1 Privacy and Security of Patient Data 325
13.6.2 Bias and Fairness in Machine Learning Models 326
13.6.3 Integration of Single-Cell Omics Data 326
13.6.4 Explainable AI for Biomarker Discovery 327
13.6.5 Personalized Medicine and Biomarker-Based Therapies 327
13.7 Conclusion 328
References 329
14 Real-Time Epilepsy Monitoring and Alerting System Using IoT Devices and Machine Learning Techniques in Blockchain-Based Environment 331 Mohsen Ghorbian and Saeid Ghorbian
14.1 Introduction 332
14.2 Preliminaries 334
14.2.1 Overview of IoT Technology 334
14.2.2 Blockchain Technology 335
14.2.3 Overview of ML Technology 336
14.2.4 Epilepsy Disease 337
14.3 IoT and ML in Healthcare 338
14.3.1 HLF Architectural Framework 338
14.3.2 Epilepsy Detection Procedures 341
14.3.3 Various Approaches to ml 342
14.4 Incorporating ML with IoT in the Blockchain 343
14.5 Intelligent Alert Mechanism in IoT Healthcare 345
14.5.1 Data Gathering, Transmission, and Storage 347
14.5.2 Analyzing Stored Data 348
14.5.3 Sending an Alert Message 349
14.6 Conclusion 351
References 352
15 Integrating Quantum Computing in Bioinformatics and Biomedical Research 357 Prasad Selladurai, Ruby Dahiya, Baskar Kandasamy and Venkateswaran Radhakrishnan
15.1 Introduction 358
15.1.1 Quantum Computing 360
15.1.2 The Role of Quantum Computing in Bioinformatics 361
15.1.3 Application of Quantum Technologies 363
15.1.4 Characteristics of Quantum Computing in Bioinformatics 364
15.1.5 What are the Tools Used in Quantum Computing in Bioinformatics? 366
15.2 Novel Approaches of Quantum Computing in Bioinformatics 367
15.2.1 Quantum Chemistry for Drug Discovery 367
15.2.2 A Quantum Advance in Genetics 369
15.2.3 Hybrid Quantum-Classical Approaches 370
15.2.4 Quantum-Inspired Machine Learning 372
15.2.5 Challenges and Limitations 374
15.3 Conclusion 375
15.4 The Future of Quantum Computing in Bioinformatics and Biomedical Research 376
References 378
16 Future Perspective and Emerging Trends in Computational Intelligence 381 Chander Prabha
16.1 Introduction 382
16.2 Emerging Trends in CI for Bioinformatics 384
16.3 ci Emerging Trends for Biomedical Systems 386
16.4 ci Future Perspective in Bioinformatics 388
16.5 The Future of CI in Biomedical Systems 391
16.6 Conclusion and Future Scope 393
References 394
Index 397
The combination of intelligent data analytics with the intricacies of biological data has become a crucial factor for innovation and growth in the fast-changing field of bioinformatics and biomedical systems. At the convergence of biology, medicine, and computational science, there is an urgent need for advanced analytical methods to interpret biological data.
The book explores the complex field of biological data analysis. It offers a thorough examination of advanced techniques, methodologies, and applications that utilize intelligence to solve biological puzzles and improve healthcare results. The introduction of high-throughput technology has resulted in a large amount of biological data, which has presented both unique issues and opportunities. The field of biological information is rapidly expanding, encompassing genomics, proteomics, clinical data, and medical imaging. This growth provides a rich environment for computational methods to uncover hidden patterns, trends, and insights within the data.
Chapter 1 delves into the progress of machine learning algorithms for analyzing biological data. The convergence of machine learning and biological data analysis has ushered in a new era of scientific exploration. This chapter examines state-of-the-art machine learning methods used with different forms of biological data, emphasizing their contribution to understanding intricate biological processes. The fusion of artificial intelligence and biology demonstrates how these methods improve our comprehension of genomes, proteomics, metabolomics, and medical uses. By providing vivid examples, this section illustrates the potential of machine learning to revolutionize biological research by pushing its limits.
Chapter 2 examines the application of predictive analytics in clinical diagnosis, and its impact on customers, healthcare professionals, and the healthcare system. Predictive analytics utilizes information, statistical formulae, and artificial intelligence to forecast and analyze disease hazards. The initial stage involves delineating the data sources, preprocessing techniques, and predictive modeling approaches employed in healthcare predictive analytics. This book demonstrates the significance of predictive analytics in the diagnosis of ailments, evaluation of risks, and provision of personalized treatment recommendations through the use of compelling application examples. In addition to image analysis, the technology offers medical practitioners accurate insights through the interpretation of pictures, making it an essential analytical tool.
Chapter 3 classifies the various categories of skin diseases. The fields of dermatology and dermoscopy present distinct difficulties due to their intrinsic intricacy and the elevated level of proficiency necessary for precise diagnosis. To tackle these difficulties, this chapter suggests a computerized method that utilizes advanced techniques in deep learning and machine learning to diagnose skin problems based on images. The goal is to surpass the constraints of traditional diagnostic procedures by utilizing cutting-edge computational technologies.
To start, simpler approaches that combined conventional machine learning models with image processing techniques were analyzed. This entailed utilizing techniques such as Histogram of Oriented Gradients (HOG) and Fast Fourier Transform (FFT) to extract features, along with image augmentation methods. The collected features were subsequently employed to train different conventional machine learning models for the categorization of skin conditions. Next, the field of deep learning is explored with training conducted on models such as ResNet18, ResNet50, EfficientNet, and InceptionNet. Compared to human procedures, these deep learning models demonstrated superior accuracy and efficiency, indicating the promise of a more dependable and efficient diagnostic system.
Chapter 4 investigates computer-aided polyp detection via a tailored Convolutional Neural Network (CNN) architecture. Identifying polyp frames manually is a time-consuming task, especially for gastroenterologists who specialize in this area. Utilizing deep learning algorithms for the automatic detection of polyps can potentially optimize the efficiency of gastroenterologists by reducing the time required for diagnosis. This chapter presents an automated approach for polyp detection with a customized CNN architecture. The development process involved significant tweaking of several parameters, including optimizers, filter size, color space, image dimension, and kernel initializers. The efficacy of the suggested design was assessed using systematic ablation research, test set evaluation, and feature mapping. The system attained optimal outcomes with increased accuracy in polyp detection, together with decreases in execution time and number of trainable parameters.
Chapter 5 delves into the modern healthcare risks that stem from computational intelligence. AI with IoT is transforming every aspect of this field, from remote patient data collection, electronic health record storage, drug delivery, research-based drug trials, pathological results synthesis or implementation, and patient and doctor functionality.
While providing great benefits, IoT with AI also poses major challenges and risks for patients and doctors in areas such as the protection of sensitive data, patient consent and EULA implementation, compliance with security standards for preserving data integrity at transit or storage, the handling the socio-economics-based digital divide in remote areas, discrepancies in multiple vendor agreements, and hierarchical implementation. This chapter lists all the important AI-based risk studies in medical IoT devices.
Chapter 6 discusses a hybrid deep learning architecture to identify and classify sleep apnea from single-channel ECG signals into four classifications. ECG waveforms were preprocessed with a discrete wavelet transform to remove noise and undesired signals. After filtering the signals, a suggested relational autoencoder compressed the information into a lower-dimensional representation to automatically extract the most relevant characteristics. The trained feature set for sleep apnea diagnosis is presented using a bidirectional long short-term memory network. The chapter compares the proposed sleep apnea diagnosis performance measurements to show efficacy.
Chapter 7 proposes Wireless Capsule Endoscopy (WCE) images for deep ensemble feature extraction-based bleeding region classification. WCE is a minimally invasive medical imaging method that provides real-time visual information about the digestive tract to diagnose gastrointestinal problems. It provides digestive system health insights with minimum discomfort and invasiveness compared to traditional endoscopic procedures. Due to illumination, artifacts, and bleeding patches, the classification in normal WCE photos can be difficult. This chapter presents an ensemble feature extraction method that uses ResNet 50, VGG 16, and Inception V3 neural networks for classification instead.
Image processing, augmentation, data pretreatment, and feature extraction can improve classification accuracy in the framework. In addition, an autoencoder reconstructs retrieved features and a Support Vector Machine (SVM) classifier helps for more accurate identification. Readers will see that our experimental results had 99% accuracy and 99.5% precision, and see how WCE imaging helps to diagnose and treat gastrointestinal diseases faster.
Chapter 8 reviews advances in brain tumor classification and localization. Medical imaging technologies like MRI and CT scans provide comprehensive tumor features, while K-means and CNNs improve detection and localization. This chapter shows the effectiveness of SVM classification and the promise of Fractional Harley transformation, It also explains how multi-CNN designs improve accuracy, whereas K-means and Fuzzy K-means improve segmentation.
CNN routinely identify brain tumors well, and deep learning, especially CNNs, performs better but can be complicated and stretch resources. This chapter highlights the evolving landscape of brain tumor detection and localization, while showcasing the power of deep learning to improve diagnostics and treatment planning.
Chapter 9 integrates the Apriori algorithm with data mining classification to improve primary tumor prediction. To improve the prediction accuracy of primary tumors based on pertinent medical variables, classification approaches are combined with the Apriori algorithm, which is known for its association rule mining capabilities. When mining frequent item sets and finding correlations in enormous datasets, the Apriori method performs exceptionally well. Through the use of identified association rules, this hybrid strategy improves prediction performance by enriching the feature set intended for categorization.
Chapter 10 deals with deep learning techniques for neurodevelopmen- tal disorders, personalized medicine, and genomics. For illness curation, patient aggregates with more unusual treatment reactions or unique medical care needs are identified using prediction medication procedures. By using contemporary computation, artificial intelligence generates a reservoir of information that enables the framework to learn and engage in clinical work with increased insight. A combination of genomic and nongenomic variables, in addition to patient-side effects, clinical history, and lifestyle data, would facilitate personalized medicine analysis and prognosis. This chapter investigates the drug combinations that will help the most challenging difficulties in personalized...
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