1st Edition
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 today’s 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:
- XAI for the applications with medical images
- XAI use cases for alternative medical data/task
- Different XAI methods for biomedical applications
- 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.
1. Gastric Cancer Detection Using Hybrid Based Network and SHAP Analysis
Varanasi L. V. S. K. B. Kasyap, D. Sumathi, and Karthika Natarajan
2. LIME Approach in Diagnosing Diseases: A Study on Explainable AI
Iyyanki Muralikrishna and Prisilla Jayanthi
3. Explainable Artificial Intelligence (XAI) in the Veterinary and Animal Sciences Field
Amjad Islam Aqib, Mahreen Fatima, Afshan Muneer, Khazeena Atta, Muhammad Arslan, C-Neen Fatima Zaheer, Sadia Muneer and Maheen Murtaza
4. Interpretable Analysis of the Potential Impact of Various Versions of Corona Virus: A Case Study
Pawan Whig and Ashima Bhatnagar Bhatia
5. XAI in Biomedical Applications
K. K. Kırboğa and E. U. Küçüksille
6. What Makes the Survival of Heart Failure Patients? Prediction by the Iterative Learning Approach and Detailed Factor Analysis with the SHAP Algorithm
A. Çifci, M. İlkuçar, and İ. Kırbaş
7. Class Activation Mapping and Deep Learning For Explainable Biomedical Applications
Prasath Alias Surendhar S., R. Manikandan, and Ambeshwar Kumar
8. Pragmatic Study of IoT In Healthcare Security with an Explainable AI Perspective
Adrija Mitra, Yash Anand, and Sushruta Mishra
9. Chest Disease Identification from X-rays Using Deep Learning
M. Hacibeyoglu and M.S. Terzi
10. Explainable Artificial Intelligence Applications in Dentistry: Theoretical Research
B. Aksoy, M. Yücel, H. Sayın, O.K.M. Salman, M. Eylence, and M.M. Özmen
11. Application of Explainable Artificial Intelligence in Drug Discovery and Drug Design
Najam-ul-Lail, Iqra Muzammil, Muhammad Aamir Naseer, Iqra Tabussam, Sidra Muzmmal and Aqsa Muzammil
12. Automatic Segmentation of Spinal Cord Gray Matter from MR Images Using U-Net Architecture
R. Polattimur and E. Dandil
13. XAI for Drug Discovery
Ilhan Uysal and Utku Kose
14. Explainable Intelligence Enabled Smart Healthcare for Rural Communities
Soumyadeep Chanda, Rohan Kumar, Aditya Kumar Singh, and Sushruta Mishra
15. Explainable Artificial Intelligence on Drug Discovery for Biomedical Applications
Godwin M. Ubi, Edu N. Eyogor, Hannah E. Etta, Nkese D. Okon, Effiom B. Ekeng, and Imabong S. Essien
16. XAI in Hybrid Classification of Brain MRI Tumor Images
S. Akça, F. Atban, Z. Garip, and E. Ekinci
17. Comparative Analysis of Breast Cancer Diagnosis Driven by a Smart IoT Based Approach
Bhavya Mittal, Pranshu Sharma, Sushruta Mishra, and Sibanjan Das
Biography
Dr. Utku Kose received his B.Sc. degree in 2008 from computer education of Gazi University, Turkey as a faculty valedictorian. He received his M.Sc. degree in 2010 from Afyon Kocatepe University, Turkey in the field of computer and D.Sc./Ph.D. degree in 2017 from Selcuk University, Turkey in the field of computer engineering. Between 2009 and 2011, he worked as a Research Assistant in Afyon Kocatepe University. He then worked as a Lecturer and Vocational School Vice Director at Afyon Kocatepe University between 2011 and 2012, as a Lecturer and Research Center Director in Usak University between 2012 and 2017, and as an Assistant Professor in Suleyman Demirel University between 2017 and 2019. Currently, he is an Associate Professor in Suleyman Demirel University, Turkey. He has written more than 200 publications, including articles, authored and edited books, proceedings, and reports. He is also on the editorial boards of many scientific journals and serves as one of the editors of the Biomedical and Robotics Healthcare book series published by CRC Press. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, biomedical applications, optimization, the chaos theory, distance education, e-learning, computer education, and computer science.
Dr. Deepak Gupta received his B.Tech. degree in 2006 from the Guru Gobind Singh Indraprastha University, Delhi, India. He received an M.E. degree in 2010 from Delhi Technological University, India, and Ph.D. degree in 2017 from Dr. APJ Abdul Kalam Technical University (AKTU), Lucknow, India. He completed his post-doc at the National Institute of Telecommunications (Inatel), Brazil, in 2018. He has co-authored more than 207 journal articles, including 168 SCI papers and 45 conference articles. He has authored/edited 60 books, published by IEEE-Wiley, Elsevier, Springer, Wiley, CRC Press, DeGruyter, and Katsons. He has filled four Indian patents. He is the convener of the ICICC, ICDAM, ICCCN, ICIIP & DoSCI Springer conferences series, and is Associate Editor of Computer & Electrical Engineering, Expert Systems, Alexandria Engineering Journal, Intelligent Decision Technologies. He is also a series editor of ""Elsevier Biomedical Engineering"" at Academic Press, Elsevier, ""Intelligent Biomedical Data Analysis"" at De Gruyter, Germany, and ""Explainable AI (XAI) for Engineering Applications"" at CRC Press. He is also serving as a startup consultant.
Dr. Xi Chen received his Ph.D. degree in 2019 from University of Kentucky, USA in the field of bioinformatics. Between 2013 and 2019, he worked as a graduate research assistant in the Department of Biochemistry, University of Kentucky. He was also a Research Collaborator at the Department of Statistics, University of Kentucky, USA, between 2017 and 2019. He was University Ambassador/Deep Learning Institute (DLI) Certified Instructor at the Nvidia Deep Learning Institute between 2018 and 2021. In 2019, he worked as a Data Scientist & Machine Learning Engineer for Verb Surgical, USA. Following to that, he was a Computational Biologist/ML Engineer Lead at Juvena Therapeutics, USA (2019–2021). Currently, he is working as a Senior Software Engineer (ML Data Foundation) at Meta, USA. His research interests include artificial intelligence, machine/deep learning, biomedical, genomics, data science, and image processing.
