Artificial Intelligence Revolutionizing Cancer Care
CRC Press (Verlag)
978-1-032-83306-4 (ISBN)
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In the ever-evolving landscape of cancer treatment, the fusion of artificial intelligence (AI) with medical science marks a groundbreaking shift toward more precise, efficient, and personalized healthcare. "Artificial Intelligence Revolutionizing Cancer Care: Precision Diagnosis and Patient-Centric Healthcare" delves into the transformative power of AI, offering a comprehensive exploration of its role in enhancing cancer diagnosis, treatment, and patient management. This edited volume brings together leading experts and researchers who illuminate the latest advancements in AI technologies applied to oncology. From machine learning algorithms that predict cancer progression to sophisticated imaging techniques that improve diagnostic accuracy, this book covers a spectrum of innovations reshaping cancer care. Key highlights include precision diagnosis, uncovering how AI-driven tools are revolutionizing the early detection and accurate classification of various cancer types, leading to better patient outcomes; patient-centric approaches, exploring the shift towards personalized medicine, where AI tailors treatment protocols to individual patient profiles, ensuring more effective and targeted therapies; and ethical and practical considerations, gaining insights into the ethical, practical, and regulatory challenges of integrating AI in healthcare, emphasizing the need for patient privacy and data security. Additionally, the book looks ahead to the potential future applications of AI in oncology, including predictive analytics, robotic surgery, and beyond. "Artificial Intelligence Revolutionizing Cancer Care" is an essential resource for medical professionals, researchers, and students seeking to understand the intersection of AI and oncology. It offers a visionary perspective on how cutting-edge technology is poised to enhance patient care and transform the fight against cancer.
This book:
Focuses on the critical intersection of artificial intelligence and cancer diagnosis within the healthcare sector.
Emphasizes the real-world impact of artificial intelligence in improving cancer detection, treatment, and overall patient care.
Covers artificial intelligence algorithms, machine learning techniques, medical image analysis, predictive modeling, and patient care applications. Explores how artificial intelligence technologies enhance the patient’s experience, resulting in better outcomes and reduced healthcare disparities. Provides readers with an understanding of the mathematics underpinning machine learning models, including decision trees, support vector machines, and deep neural networks.
It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, biomedical engineering, and information technology.
Suman Kumar Swanrkar received a Ph.D. (CSE) degree in 2021 from Kalinga University, Nayar Raipur. He received M.Tech. (CSE) degree in 2015 from the Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India. He has 2+ year of experience in IT industry as Software Engineer and 6+ year of experience in Educational Institutes as Assistant Professor. Currently associated with Shri Shankaracharya Institute of Professional Management and Technology, raipur as Assistant Professor in Computer Science & Engineering Department. He Has Guided 5+ MTech Scholars and some of undergoing. He has Published and grant Indian/Australian patent, some are waiting for grant. He has authored and co-authored of more than 15 journal articles including WOS & Scopus papers Presented research paper in 3 international conferences. He has Contributed to book chapter, published by publications of international repute. He has lifetime Membership of IEEE, IAENG, ASR, IFERP, ICSES, Internet Society, UACEE, IAOP, IAOIP, EAI, CSTA. He has Successfully completed many FDP, Training, webinar & Workshop and Completed the 2-Weeks comprehensive online Patent Information Course. Proficiency in handling the Teaching, Research as well as administrative activities. He has contributed massive literature in the fields of Intelligent Data Analysis, Nature-Inspired Computing, Machine Learning and Soft Computing. Abhishek Guru received a Ph.D. (CSE) degree in 2021 from Kalinga University, Naya Raipur. He received his MSc. (CS) degree in 2012 from Makhanlal Chaturvedi Rashtriya Patrakarita Vishwavidyalaya, Bhopal, India. He has 3 Months of experience in IT industry as a Software Engineer and 10.1 years of experience in educational institutes as an Assistant Professor. Currently associated with KL Deemed to Be University, Green Fields, Vaddeswaram, India as Assistant Professor in Computer Science & Engineering Department. He has published and granted Indian/Australian patents, some are waiting for grants. He has authored and co-authored of more than 10 journal articles including WOS & Scopus papers Presented research papers in 2 international conferences. He has contributed to book chapters, published by reputed international publishers and has lifetime Membership of IAENG, ASR, IFERP, ICSES, Internet Society, UACEE, IAOIP, EAI, CSTA. He has successfully completed many FDP, Training, webinar & workshops. and also Completed the 2-Weeks comprehensive online Patent Information Course. Proficiency in handling Teaching, Research as well as administrative activities. He has contributed massive literature in the fields of Network Security, Cyber Security, Cryptography, and IoT. Gurpreet Singh Chhabra (Ph.D. CSE) has more than 15 years of teaching experience and is currently working as an Assistant Professor in the Computer Science & Engineering department at GITAM School of Technology, GITAM Deemed to be University, Visakhapatnam. He has research interests in Deep Learning, Machine Learning, Data Science, and Fog Computing. He is a life member of the ISTE (Indian Society for Technical Education) and IAENG (International Association of Engineers). Also, He has credit for many national and international papers, patents, books, and book chapters. His qualifications are fortified with a great deal of creativity and problem-solving skills. Harshitha Raghavan Devarajan is an AI researcher with an unwavering passion for revolutionizing the healthcare industry through the potential of advanced technology. Graduating from New York University, he has consistently sought out opportunities to engage in meaningful research and contribute to technological advancements. His journey embodies the synergy between a deep-rooted commitment to pushing the boundaries of innovation and a profound understanding of the transformative power of artificial intelligence. With "Reimagining Healthcare: AI's Journey to Transforming an Industry," he invites the readers to explore his experiences and insights as he strives to reshape healthcare through the lens of cutting-edge AI, aiming to make a lasting impact on countless lives.
1. K-Means Clustering for Knowledge Discovery in Big Data Cancer Research. 2. Applying Reinforcement Learning to Optimize Cancer Treatment Protocols in Machine Learning Frameworks 3. Extraction of Real-Time Data of Breast Cancer Patients and Implementation with ML Techniques. 4. Decoding Images Convolutional Neural Networks in Oncological Medical Imaging. 5. Uncovering Insights in Cancer Research with Centroid-Based Clustering on Big Data. 6. The Role of Machine Learning in Remote Cancer Management: A Systematic Review. 7. Revolutionizing Cancer Drug Discovery Deep Learning Neural Networks for Accelerated Development. 8. Empowering Patients Enhancing Engagement And Self-Care In Cancer Treatment With Bayesian Networks. 9. Enhancing Cancer Detection and Classification with Ensemble Machine Learning Approaches. 10. Ethics, Regulation, and Machine Learning Navigating Oncological AI Deployment with Decision Trees. 11. A Comprehensive Review of Big Data Integration and K-Means Clustering in Cancer Research. 12. Applications of Generative Adversarial Networks (GANs) in Healthcare. 13. Performance Analysis of Stochastic Gradient Descent and Adaptive Moment Estimation Optimization Algorithms for Convolutional Neural Networks. 14. Enhancing Oncology with Predictive Analytics for Cancer Diagnosis and Treatment with Random Forests. 15. Automated Diagnosis of Brain Tumors from MRI Scans Using U-Net Segmentation.
| Erscheint lt. Verlag | 25.2.2025 |
|---|---|
| Reihe/Serie | Future Generation Information Systems |
| Zusatzinfo | 38 Tables, black and white; 55 Line drawings, black and white; 7 Halftones, black and white; 62 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Software Entwicklung |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 1-032-83306-8 / 1032833068 |
| ISBN-13 | 978-1-032-83306-4 / 9781032833064 |
| Zustand | Neuware |
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