Explainable Artificial Intelligence in Medical Imaging

Auteur

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.

Texte du rabat

The book presents a thorough review of state-of-the-art techniques for precise analysis and diagnosis with an emphasis on explainable artificial intelligence and its applications in healthcare. Researchers, academics, business professionals, health practitioners, and students will all benefit from the knowledge in this book.

Contenu

1. Explainable Artificial Intelligence in Medicine: Social and Ethical Issues 2. Explainable AI for Diagnosis of Pneumonia Using Chest X-ray Images: Current Achievements and Analysis on Benchmark Datasets 3. Explainable AI for Medical Science: A Comprehensive Survey, Current Challenges, and Possible Directions 4. Explainable Artificial Intelligence Techniques in Healthcare Applications 5. Automatic Detection of Leukemia Through Explainable AI-Based Machine Learning Approaches: Directional Review 6. Improvement Alzheimer's Segmentation by VGG16 and U-Net Autoencoder Techniques 7. Skin Cancer Detection and Classification Using Explainable Artificial Intelligence for Unbalanced Data: State of the Art 8. Enhancing Heart Disease Diagnosis with XAI-Infused Ensemble Classification 9. Transparency in HealthTech: Unveiling the Power of Explainable AI 10. Therapeutic Virtual Reality Exposure Therapies for Nyctophobia and Claustrophobia with Active Heart Rate Monitoring 11. Explainable Artificial Intelligence-Based Machine Analytics and Deep Learning in Medical Science 12. Revolutionizing Prostate Cancer Diagnosis: Vision Transformers with Explainable Artificial Intelligence to Accurate and Interpretable Prostate Cancer Identification