Full Description
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 toward 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.
Contents
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.
