1st Edition
Explainable Artificial Intelligence in Medical Imaging Fundamentals and Applications
Artificial intelligence (AI) in medicine is rising, and it holds tremendous potential for more accurate findings and novel solutions to complicated medical issues. Biomedical AI has potential, especially in the context of precision medicine, in the healthcare industry’s next phase of development and advancement. Integration of AI research into precision medicine is the future; however, the human component must always be considered.
Explainable Artificial Intelligence in Medical Imaging: Fundamentals and Applications focuses on the most recent developments in applying artificial intelligence and data science to health care and medical imaging. Explainable artificial intelligence is a well-structured, adaptable technology that generates impartial, optimistic results. New healthcare applications for explicable artificial intelligence include clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms. This book overviews the principles, methods, issues, challenges, opportunities, and the most recent research findings. It makes the emerging topics of digital health and explainable AI in health care and medical imaging accessible to a wide audience by presenting various practical applications.
Presenting a thorough review of state-of-the-art techniques for precise analysis and diagnosis, the book emphasizes explainable artificial intelligence and its applications in healthcare. The book also discusses computational vision processing methods that manage complicated data, including physiological data, electronic medical records, and medical imaging data, enabling early prediction. Researchers, academics, business professionals, health practitioners, and students all can benefit from this book’s insights and coverage.
1. Explainable Artificial Intelligence in Medicine: Social & Ethical Issues
Shahid Naseem, Tariq Mahmood, Hannan Bin Liaqat, Amjad R. Khan, and Umer Farooq
2. Explainable AI for Diagnosis of Pneumonia Using Chest X-Ray Images: Current Achievements and Analysis on Benchmark Datasets
Muhammad Mujahid, Tanzila S. Khan, Fetoun Alzahrani, Abrar Wafa, and Abeer Rashad Mirdad
3. Explainable AI for Medical Science: A Comprehensive Survey, Current Challenges, and Possible Directions
Deep Kothadiya, Chintan Bhatt, Amjad R. Khan, Amerah Alghanim, and Fatima Nayer Khan
4. Explainable Artificial Intelligence Techniques in Healthcare Applications
Hareem Ayesha, Sajid Iqbal, Mehreen Tariq, Abdullah Alaulamie, and Aiesha Ahmad
5. Automatic Detection of Leukemia Through Explainable AI-Based Machine Learning Approaches: Directional Review
Rida Arif, Shahzad Akbar, Sahar Gull, Qurat Ul Ain, and Noor Ayesha
6. Improvement Alzheimer's Segmentation by VGG16 and U-Net Autoencoder Techniques
Karrar A Kadhim, Farhan Mohamed, and Ghalib Ahmed Salman
7. Skin Cancer Detection and Classification Using Explainable Artificial Intelligence for Unbalanced Data: State of the Art
Ahmad Bilal Farooq, Shahzad Akbar, Qurat ul Ain, Zunaira Naqvi, and Farwa Urooj
8. Enhancing Heart Disease Diagnosis with XAI-Infused Ensemble Classification
Naveed Abbas, Talha Tasleem, Abdul Hai, Zieb Rabie Alqahtani, and Bandar Ali Mohammed Alrami Alghamdi
9. Transparency in HealthTech: Unveiling the Power of Explainable AI
Shiza Maham, Abdullah Tariq, Muhammad Usman Ghani Khan, and Amjad R. Khan
10. Therapeutic Virtual Reality Exposure Therapies for Nyctophobia and Claustrophobia with Active Heart Rate Monitoring
Zubaira Naz, Ayesha Azam, Muhammad Usman Ghani Khan, and Noor Ayesha
11. Explainable Artificial Intelligence-Based Machine Analytics and Deep Learning in Medical Science
Morteza Soltani, Mehdi Davari, Mina Bahadori, Ahmad Kokhahi, Mahsa Bahadori, and, Masoumeh Soleimani
12. Revolutionizing Prostate Cancer Diagnosis: Vision Transformers with Explainable Artificial Intelligence to Accurate and Interpretable Prostate Cancer Identification
Krunal Maheriya, Mrugendrasinh Rahevar, Martin Parmar, Deep Kothadiya, Atul Patel, and Amit Ganatra
Biography
Amjad Rehman Khan (Senior Member, IEEE) earned a Ph.D. from the Faculty of Computing, Universiti Teknologi Malaysia (UTM), Malaysia, specializing in information security using image processing techniques in 2010. He received a Rector Award for the 2010 Best Student from UTM Malaysia. He is currently associate professor at CCIS Prince Sultan University Riyadh, Saudi Arabia. He is also a principal investigator in several projects and completed projects funded by MoHE Malaysia, Saudi Arabia. His research interests are bioinformatics, IoT, information security, and pattern recognition.
Tanzila Saba (Senior Member, IEEE) received his Ph.D. degree in document information security and management from the Faculty of Computing, Universiti Teknologi Malaysia (UTM), Malaysia, in 2012. She is currently a full professor with the College of Computer and Information Sciences, Prince Sultan University (PSU), Riyadh, Saudi Arabia, and also the leader of the AIDA Laboratory. She has published over 300 publications in high-ranked journals. Her primary research interests include bioinformatics, data mining, and classification using AI models. She received the Best Student Award from the Faculty of Computing, UTM, in 2012 and also received the best researcher award from PSU, from 2013 to 2016. She is the editor of several reputed journals and on a panel of TPC of international conferences.
