Recent Advances in AI-enabled Automated Medical Diagnosis

Developments in deep learning in the past decade have led to phenomenal growth in AI-based automated medical diagnosis, opening a door to a new era of both medical research and medical industry. It is a golden age for researchers involved in the development and application of advanced machine learning techniques for medical and clinical problems. This book captures the most recent important advances in this cross-disciplinary topic and brings the latest advances to a wide audience including experts, researchers, students, industry developers and medical services.

1. Enhancement of COVID-19 Diagnosis Using Machine Learning and Texture Analyses of Lung Imaging Bhuvan Mittal, JungHwan Oh 2. Modeling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification Perspectives Hua-Liang Wei, Stephen A. Billings 3. Deep Learning-based Respiratory Anomaly and COVID Diagnosis Using Audio and CT Scan Imagery Conor Wall, Chengyu Liu and Li Zhang 4. COVID-19 Forecasting in India through Deep Learning Models Arindam Chaudhuri and Soumya K. Ghosh 5. Deep Learning-based Techniques in Medical Imaging for COVID-19 Diagnosis: A Survey Fozia Mehboob, Abdul Rauf, Khalid M. Malik, Richard Jiang, Abdul K.J. Saudagar, Abdullah AlTameem and Mohammed AlKhathami 6. Embedding Explainable Artificial Intelligence in Clinical Decision Support Systems: The Brain Age Prediction Case Study Angela Lombardi, Domenico Diacono, Nicola Amoroso, Alfonso Monaco, Sabina Tangaro and Roberto Bellotti 7. Machine Learning-based Biological Ageing Estimation Technologies: A Survey Zhaonian Zhang, Richard Jiang, Danny Crookes and Paul Chazot 8. Review on Social Behavior Analysis of Laboratory Animals: From Methodologies to Applications Ziping Jiang, Paul L. Chazot and Richard Jiang 9. Acute Lymphoblastic Leukemia Diagnosis Using Genetic Algorithm and Enhanced Clustering-based Feature Selection Siew Chin Neoh, Srisukkham Worawut, Li Zhang and Md. Mostafa Kamal Sarker 10. Artificial Intelligence-enabled Automated Medical Prediction and Diagnosis in Trauma Patients Lianyong Li, Changqing Zhong, Gang Wang, Wei Wu, Yuzhu Guo, Zheng Zhang, Bo Yang, Xiaotong Lou, Ke Li and Heming Yang 11. DCGAN-based Facial Expression Synthesis for Emotion Well-being Monitoring with Feature Extraction and Cluster Grouping Eaby Kollonoor Babu, Kamlesh Mistry and Li Zhang 12. A Hybrid-DE for Automatic Retinal Image-based Blood Vessel Segmentation Colin Paul Joy, Kamlesh Mistry, Gobind Pillai and Li Zhang 13. Artificial Intelligence for Accurate Detection and Analysis of Freezing of Gait in Parkinson’s Disease Wenting Yang, Simeng Li, Debin Huang, Hantao Li, Lipeng Wang, Wei Zhang and Yuzhu Guo 14. Sparse Model Identification for Nonstationary and Nonlinear Neural Dynamics Based on Multiwavelet Basis Expansion Song Xu, Lina Wang and Jingjing Liu 15. How Weather Conditions Affect the Spread of COVID-19: Findings of a Study Using Contrastive Learning and NARMAX Models Yiming Sun and Hua-Liang Wei 16. New Measurement of the Body Mass Index with Bioimpedance Using a Novel Interpretable Takagi-Sugeno Fuzzy NARX Predictive Model Changjiang He, Yuanlin Gu, Hua-Liang Wei and Qinggang Meng 17. Training Therapy with BCI-based Neurofeedback Systems for Motor Rehabilitation Jingjing Luo, Qiying Chen, Hongbo Wang, Youhao Wang and Qiang Du 18. A Modified Dynamic Time Warping (MDTW) and Innovative Average Non-self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings Xinxin Yao and Hua-Liang Wei 19. An Investigation on ECG-based Cardiological Diagnosis via Deep Learning Models Alex Meehan, Zhaonian Zhang, Bryan Williams and Richard Jiang 20. EEG-based Deep Emotional Diagnosis: A Comparative Study Geyi Liu, Zhaonian Zhang, Richard Jiang, Danny Crookes and Paul Chazot 21. A Novel Motor Imagery EEG Classification Approach Based on Time-Frequency Analysis and Convolutional Neural Network Qinghua Wang, Lina Wang and Song Xu 22. Classification of EEG Signals for Brain-Computer Interfaces using a Bayesian-Fuzzy Extreme Learning Machine Adrian Rubio-Solis, Carlos Beltran-Perez and Hua-Liang Wei

Richard Jiang is a Senior Lecturer (Associate Professor) in the School of Computing & Communications at Lancaster University, UK. He holds a Leverhulme Trust Research Fellowship. He is a Fellow of HEA, an Associate Member of EPSRC College, and an EPSRC RISE Connector. Dr Jiang's principal research interests are in Artificial Intelligence, XAI, Biometrics, Privacy & Security, Intelligent Systems, and Biomedical Image Analysis. His recent research has been supported by grants from EPSRC (EP/P009727/1), Leverhulme Trust (RF-2019-492), Qatar National Research Fund (NPRP No.8–140-2–065) and other industry/international funders. He has supervised and co-supervised several PhD students. He authored over 80 publications and was the lead editor of three books published by Springer. He served as a TPC/Editorial member and a reviewer for various international conferences and journals. Li Zhang is a Reader in Department of Computer Science, Royal Holloway, University of London, UK. She received a PhD degree from the University of Birmingham, UK. She has expertise in machine learning, deep learning, computer vision, and intelligent robotics. Dr Zhang’s recent research has been funded by Innovate UK (2017-2019, 2018-2020, 2020-2021), Research England (2020-2021), and European Commission (2012-2016, 2014-2018, 2018-2021, 2019-2022). She is the lead Guest Editor for Complexity, Springer Multimedia Tools and Applications and Elsevier Pattern Recognition Letters. She has supervised and co-supervised several PhD students. Dr Zhang is a Senior Member of IEEE. Hua-Liang Wei is a Senior Lecturer (Associate Professor) with the Department of Automatic Control and Systems Engineering (ACSE), the University of Sheffield, Sheffield, UK. He is head of the Digital Medicine & Computational Neuroscience (DMCN), head of the laboratory of Dynamic Modelling, Data Mining and Decision Making (3DM), and a key member of the Insigneo Institute for in silico medicine and the Neuroscience Institute of the University of Sheffield. He has been awarded grants (as PI and Co-I) from EPSRC, NERC, Norwegian Research Council, EU H2020, Ryder Briggs Charity, and the Royal Society. He received the Ph.D. degree in the Department of Automatic Control and Systems Engineering, the University of Sheffield, UK, in 2004. He has published more than 120 papers (Google Scholar H index 34 and almost 3800 citations) since the completion of the PhD in 2004. Dr Wei’s research interests include nonlinear system identification, machine learning, neural networks, interpretable machine learning, deep learning, computational intelligence, AI and its applications, data-driven modelling, data mining, pattern recognition, data based and data informed predictive modelling, with applications in medical research and AI-assisted diagnosis among other areas. Danny Crookes is an Emeritus Professor at Queen’s University Belfast (QUB), where was earlier the Head of the computer science department from 1993 to 2002 and was also appointed to the Chair of the Computer Engineering. He was also the Director of the Research for Speech, Image, and Vision Systems at the Institute for Electronics, Communications, and Information Technology (ECIT), QUB before he retired, in 2017. He is currently an Emeritus Professor in computer science with QUB. He has published more than 200 scientific articles in journals and international conferences, and has presented tutorials on parallel image processing at several international conferences. His current research interests include the use of novel architectures (especially GPUs) for high-performance image processing. Professor Crookes has applied expertise in language design, optimizing compilers and software generators, plus software tools for hardware description, and architecture generation, to the goal of developing high level software tools to enable rapid development of real-time video processing systems. He has been currently involved in projects in automatic shoeprint recognition (ESPRC), speech separation and enhancement (EPSRC), and processing of 4D confocal microscopy imagery (sponsored by INI and Andor Technologies). Paul Chazot is an Associate Professor of Neuropharmacology in Biosciences, Durham University. He is leading a research group focusing on the identification, characterisation and validation of novel drug targets for the treatment of the major acute and chronic CNS pathologies. He also dedicates to developing novel all-in-one behavioural tests for both animals and humans. Dr Chazot has developed two new clinical development programmes over the last 20 years, one for chronic pain (Votucalis), and one for Alzheimer’s Disease (PBM-T 1068nm), the former at the pipeline stage for the company Akari Therapeutics UK, and the latter reaching the FDA Phase 2b clinical stage in the US. He is also the Durham lead for the Durham University spin-out company Nevrargenics, who has developed exciting new rational drug leads (RAR-M) for dis