Explainable artificial intelligence for biomedical applications
Since its first appearance, artificial intelligence has been ensuring revolutionary outcomes in the context of real-world problems. At this point, it has strong relations with biomedical and today⁰́₉s intelligent systems compete with human capabilities in medical tasks. However, advanced use of arti...
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| Other Authors: | , , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
[United States] :
River Publishers,
[2023]
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| Series: | River Publishers series in biomedical engineering.
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| Subjects: | |
| ISBN: | 9788770228848 8770228841 9781003810582 1003810586 9781032629353 1032629355 9781003810605 1003810608 9788770228497 8770228493 |
| Physical Description: | 1 online resource. |
Cover
Table of contents
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| 245 | 0 | 0 | |a Explainable artificial intelligence for biomedical applications / |c editor, Utku Kose, Deepak Gupta, Xi Chen. |
| 264 | 1 | |a [United States] : |b River Publishers, |c [2023] | |
| 264 | 4 | |c ©2023 | |
| 300 | |a 1 online resource. | ||
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| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a River Publishers series in biomedical engineering | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a Since its first appearance, artificial intelligence has been ensuring revolutionary outcomes in the context of real-world problems. At this point, it has strong relations with biomedical and today⁰́₉s intelligent systems compete with human capabilities in medical tasks. However, advanced use of artificial intelligence causes intelligent systems to be black-box. That situation is not good for building trustworthy intelligent systems in medical applications. For a remarkable amount of time, researchers have tried to solve the black-box issue by using modular additions, which have led to the rise of the term: interpretable artificial intelligence. As the literature matured (as a result of, in particular, deep learning), that term transformed into explainable artificial intelligence (XAI). This book provides an essential edited work regarding the latest advancements in explainable artificial intelligence (XAI) for biomedical applications. It includes not only introductive perspectives but also applied touches and discussions regarding critical problems as well as future insights. Topics discussed in the book include: ⁰́Ø XAI for the applications with medical images ⁰́Ø XAI use cases for alternative medical data/task ⁰́Ø Different XAI methods for biomedical applications ⁰́Ø Reviews for the XAI research for critical biomedical problems. Explainable Artificial Intelligence for Biomedical Applications is ideal for academicians, researchers, students, engineers, and experts from the fields of computer science, biomedical, medical, and health sciences. It also welcomes all readers of different fields to be informed about use cases of XAI in black-box artificial intelligence. In this sense, the book can be used for both teaching and reference source purposes. | ||
| 505 | 0 | |a Preface xiii Foreword xvii Acknowledgement xix List of Contributors xxi List of Figures xxv List of Tables xxxiii List of Abbreviations xxxv 1 Gastric Cancer Detection using Hybrid-based Network and SHAP Analysis 1 1.1 Introduction 2 1.2 XAI Approaches 4 1.2.1 Model agnostic vs. model specific 5 1.2.2 Local and global methods 5 1.2.3 Pre-model, in-model, and post-model 5 1.2.4 Visualization or surrogate methods 6 1.2.5 Approaches 6 1.3 Materials and Methods 7 1.3.1 Data processing and augmentation 7 1.3.2 Multi-scale network (MSN) module 8 1.3.3 Inner network (In-Net) module 8 1.3.4 Slice-based classification (SC-Net) module 9 1.3.5 Implementation of the proposed network 9 1.4 Experiments and Results 10 1.4.1 BOT gastric dataset 10 1.4.2 Results 11 1.5 Conclusion 13 References 13 2 LIME Approach in Diagnosing Diseases ⁰́₃ A Study on Explainable AI 17 2.1 Introduction 17 2.2 XAI Model for Predicting Heart Attack 19 2.3 XAI for Opthalmology 22 2.4 LIME ⁰́₃ Local Interpretable Model-Agnostic Explanations 23 2.4.1 LIME approach for predicting COVID-19 24 2.4.2 Prediction of thyroid using LIME approach 25 2.4.3 LIME method ⁸́₂ air pollutant industries 25 2.4.4 Binary classification of breast cancer ⁸́₂ LIME method 26 2.5 Multiclassification of ECG Signals using GRAD-CAM 27 2.6 Conclusion 28 Acknowledgments 29 References 29 3 Explainable Artificial Intelligence (XAI) in the Veterinary and Animal Sciences Field 33 3.1 Introduction 34 3.2 Mechanism of Explainable Artificial Intelligence in Biomedical Application 36 3.3 XAI in Diagnosis, Prevention, and Treatment 37 3.4 XAI in Dairy Farming 39 3.5 XAI in Poultry Farming 42 3.5.1 Poultry drones 43 3.5.2 Avian illness detection models 44 3.5.3 Models for detecting behavioral disorders 44 3.6 Conclusion 49 References 50 4 Interpretable Analysis of the Potential Impact of Various Versions of Corona Virus: A Case Study 57 4.1 Introduction 58 4.2 Modeling using Machine Learning 60 4.2.1 Multiclass classification 61 4.2.2 Multi-output regression models with multiple models 63 4.2.3 Different expert models 64 4.2.4 Hybrid models 64 4.3 Sentimentality Analysis 65 4.3.1 Sentiment analysis techniques 66 4.3.2 Sentiment analysis across languages 67 4.4 Case Study Discussion 67 4.5 Model Interpretation 74 4.5.1 Results and discussion 74 4.6 Conclusion and Future Scope 75 Acknowledgements 75 References 75 5 XAI in Biomedical Applications 79 5.1 Introduction 79 5.2 Main Text 80 5.2.1 Main biomedical goals of XAI 80 5.2.2 Use of XAI in Biomedical Studies 83 5.3 Limitations and Future Direction 93 5.4 Conclusion 94 References 95 6 What Makes Survival of Heart Failure Patients? Prediction by the Iterative Learning Approach and Detailed Factor Analysis with the SHAP Algorithm 101 6.1 Introduction 102 6.2 Related Works Using Heart Failure Dataset 103 6.3 Materials and Methods 105 6.3.1 Heart failure dataset 105 6.3.2 Overview of artificial neural networks 105 6.4 Results 110 6.5 Conclusion 117 References 118 7 Class Activation Mapping and Deep Learning for Explainable Biomedical Applications 123 7.1 Introduction 124 7.2 Background Study 127 7.3 Discussion 132 7.4 Conclusion 136 References 137 8 Pragmatic Study of IoT in Healthcare Security with an Explainable AI Perspective 145 8.1 Introduction 145 8.2 Objective 147 8.3 Motivation 147 8.4 Standards for Cybersecurity 148 8.5 Comparison of Existing Relevant Models 149 8.5.1 Secure explainable intelligent model for smart healthcare under block-chain framework 150 8.5.2 Secure IoT healthcare with access control based on explainable deep learning 155 8.6 Comparative Analysis of the Models 160 8.7 Using explainable AI in IoT security 162 8.8 Conclusion 164 References 164 9 Chest Disease Identification from X-rays using Deep Learning 167 9.1 Introduction 168 9.2 Deep Learning 170 9.2.1 Convolutional neural networks 171 9.2.2 Dataset 176 9.2.3 Experimental study 179 9.3 Conclusion 180 References 184 10 Explainable Artificial Intelligence Applications in Dentistry: A Theoretical Research 189 10.1 Introduction 189 10.1.1 An overview of the dentistry 190 10.1.2 Imaging techniques in the dentistry 191 10.1.3 Problem solving with artificial intelligence in dentistry images 192 10.2 Imaging Techniques using X-rays 193 10.3 CBCT Imaging 195 10.4 Artificial Intelligence Techniques in Dental Applications 197 10.4.1 Explainable artificial intelligence 198 10.4.2 The importance of artificial intelligence and explainable artificial intelligence in dental practices 200 10.5 Academic Studies in the Dentistry 201 10.6 Conclusion 203 References 204 11 Application of Explainable Artificial Intelligence in Drug Discovery and Drug Design 213 11.1 Introduction 214 11.1.1 Drug discovery 214 11.1.2 Explainable artificial intelligence 214 11.2 Deep Learning and Machine Learning 217 11.2.1 Support vector machines 219 11.2.2 Random forests 220 11.2.3 K-nearest neighbor 220 11.2.4 Na©¯ve Bayes approach 220 11.2.5 Restricted Boltzmann machine 221 11.2.6 Deep belief networks 221 11.2.7 Conventional neural networks 221 11.2.8 Advantages of deep learning 222 11.2.9 Limitations of deep learning 222 11.2.10 Neurosymbolic models 222 11.2.11 Advances in XAI 225 11.2.12 Different XAI approaches that aid in drug discovery 226 11.2.13 Limitations of XAI in drug discovery 232 11.3 Conclusion 232 References 233 12 Automatic Segmentation of Spinal Cord Gray Matter from MR Images using a U-Net Architecture 245 12.1 Introduction 246 12.2 Materials and Methods 250 12.2.1 Spinal cord dataset 250 12.2.2 Dataset organization and image pre-processing 252 12.2.3 U-Net 252 12.3 Experimental Results 254 12.4 Conclusions and Discussions 258 Acknowledgements 260 References 260 13 XAI for Drug Discovery 265 13.1 Introduction 265 13.2 Main Text 268 13.2.1 The working principle of explainable artificial intelligence 269 13.2.2 Current methods in the scope of explainable artificial intelligence 270 13.2.3 Approaches in XAI with drug discovery 273 13.3 Conclusion 280 References 281 14 Explainable Intelligence Enabled Smart Healthcare for Rural Communities 289 14.1 Introduction 289 14.2 Relevant Models for Smart Rural Healthcare 291 14.2.1 An interpretable emergency ambulance response model 291 14.2.2 An explainable farmer health insurance model 295 14.2.3 A bridge between NGO and hospitals model 298 14.3 An Explainable AI Approach to Smart Rural Healthcare 303 14.4 Conclusion 304 References 305 15 Explainable Artificial Intelligence in Drug Discovery for Biomedical Applications 309 15.1 Introduction 310 15.2 Methodology 311 15.2.1 Tramadol drug 312 15.2.2 Cannabinol drug 318 15.2.3 Sildenafil (Viagra) drug 319 15.2.4 Praziquantel drug 325 15.3 Discussion 329 15.4 Conclusion 330 Acknowledgements 331 References 331 16 XAI in the Hybrid Classification of Brain MRI Tumor Images 337 16.1 Introduction 338 16.2 Materials and Methods 340 16.2.1 Dataset 340 16.2.2 Method 340 16.3 Results 343 16.3.1 Performance metrics and confusion matrix 343 16.3.2 K-fold cross-validation 345 16.3.3 Results of simulation 345 16.3.4 Discussions 346 16.4 Conclusion 349 References 349 17 Comparative Analysis of Breast Cancer Diagnosis Driven by the Smart IoT-based Approach 353 17.1 Introduction 354 17.1.1 Objective 356 17.2 Analysis of Computational Techniques for Breast Cancer Diagnosis using IoT 357 17.2.1 Smart breast cancer diagnosis using machine learning 357 17.2.2 A deeper analysis of how deep learning is a step ahead of machine learning 360 17.2.3 Breast cancer diagnosis using deep learning and IoT 362 17.2.4 Challenges of breast cancer diagnosis using modern techniques 365 17.2.5 Future scope and application 365 17.3 Conclusion 370 Acknowledgements 371 References 371 Index 375 About the Editors 379. | |
| 590 | |a Knovel |b Knovel (All titles) | ||
| 650 | 0 | |a Artificial intelligence |x Medical applications. | |
| 650 | 0 | |a Biomedical engineering. | |
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| 655 | 9 | |a electronic books |2 eczenas | |
| 700 | 1 | |a Kose, Utku, |d 1985- |e editor. | |
| 700 | 1 | |a Gupta, Deepak, |c Ph.D., |e editor. | |
| 700 | 1 | |a Chen, Xi, |e editor. | |
| 776 | 0 | 8 | |i Print version: |z 8770228493 |z 9788770228497 |w (OCoLC)1385400642 |
| 830 | 0 | |a River Publishers series in biomedical engineering. | |
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