Classical computers encode information in binary bits that can either be 0s or 1s. In a quantum computer, the basic unit of memory is a quantum bit or qubit. These qubits play a similar role in terms of storing information, but use physical systems, such as the spin of an electron or the orientation of a photon, to do so. In situations where there are a large number of possible feature combinations, quantum computers can consider them simultaneously, speeding up the data processing time.
In healthcare, where there are often large numbers of possible factors to consider, quantum computers can address them simultaneously, thereby allowing doctors to compare much, much more data, and all permutations of that data, in parallel to discover the best patterns that describe it, and therefore predict the best treatment options.
This edited book explores the field of quantum computing and machine learning for medical data processing, and is a useful resource for computer engineers, researchers, healthcare technologists and scientists specialising in quantum computing, quantum AI, data processing, deep learning, machine learning, smart healthcare, and medical data systems.
This edited book covers the field of quantum computing and machine learning for medical data processing. Topics such as security, transparency, data integrity, big data and applications in drug discovery, precision in diagnostics and precision medicine, are explored.
- Chapter 1: Blockchain in Healthcare: Revolutionizing Security, Transparency, and Efficiency
- Chapter 2: CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
- Chapter 3: Harmony in Health: Machine Learning Empowered Phonocardiogram Analysis for Early Cardiovascular Disease Detection
- Chapter 4: Quantum Artificial Intelligence for Healthcare, Supply Chain and Smart City Applications
- Chapter 5: Image Segmentation Algorithms for Healthcare Applications: Enhancing Precision in Diagnostics and Treatment
- Chapter 6: Deep Learning for Medical Image Processing: A Comprehensive Exploration of Applications and Challenges
- Chapter 7: Blockchain in Healthcare: A Comprehensive Exploration of Security, Interoperability, and Data Integrity
- Chapter 8: Big Data Security and Privacy in Health care
- Chapter 9: Neural Rhythms: Unveiling Pathways to Early Detection in Neurological Disorders through Wearable EEG Analysis
- Chapter 10: Quantum Computing Applications in Healthcare: Revolutionizing Diagnosis, Treatment, and Data Security
- Chapter 11: Quantum Computing in Healthcare: Exploring Applications for Drug Discovery and Precision Medicine
- Chapter 12: An Intelligent Skin Cancer Disease Identification Strategy using Style-GAN Algorithm
- Chapter 13: Importance and Need of IoMT and Big Data to Revolutionizing Healthcare Industry
- Chapter 14: Quantum Blockchain-Oriented Data Integrity Scheme for Validating Clinical Datasets
- Chapter 15: Quantifying the Performance of Quantum Machine Learning Algorithms for Heart Valve Detection Using H-Bert Classifier
- Chapter 16: Quantum-Informed AI: Precision Caloric Assessment for Optimal Health Through Advanced Nutrition Analysis in Lifestyle Management
Abhishek Kumar is an associate professor in the CSE department at Chandigarh University, Punjab, India, and a post-doctoral fellow at the Ingenium Research Group Lab, Universidad De Castilla-La Mancha, Ciudad Real, Spain. He has more than 13 years of academic teaching experience and over 170 publications in peer-reviewed national and international journals and conferences.
Ashutosh Kumar Dubey is an associate professor in the Department of Computer Science at Chitkara University School of Engineering and Technology, situated in Himachal Pradesh, India. He is a post-doctoral fellow at the Ingenium Research Group Lab, Universidad de Castilla-La Mancha, Ciudad Real, Spain. His research interests encompass machine learning, renewable energy, health informatics, nature-inspired algorithms, cloud computing, and big data.
Vincenzo Piuri is a professor of computer engineering at the University of Milan, Italy, an associate professor at the Polytechnic of Milan, Italy and visiting professor at the University of Texas at Austin, USA, and visiting researcher at George Mason University, USA and honorary professor at Obuda University, Hungary; Guangdong University of Petrochemical Technology, China; Northeastern University, China; Muroran Institute of Technology, Japan; Amity University, India; and Galgotias University, India. He is a fellow of the IEEE, a distinguished scientist of ACM, and a senior member of INNS. His research interests include neural networks, biometrics, and image processing.
Joel J.P.C. Rodrigues is with the Amazonas State University, Brazil. He is also the leader of the Center for Intelligence at Fecomércio/CE, Brazil and full professor at Lusófona University, Lisbon, Portugal. Prof. Rodrigues is a Highly Cited Researcher (Clarivate), and leader of NetGNA research group (CNPq). He has authored more than 1250 contributions and he is the EiC of the International Journal of E-Health and Medical Communications.
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