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Artificial Intelligence Revolutionizing Cancer Care. Precision Diagnosis and Patient-Centric Healthcare

ISBN: 9781032833064
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Διαστάσεις 23 × 15 cm
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Περιγραφή

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.

Περιεχόμενα

Chapter 1: K-Means Clustering for Knowledge Discovery in Big Data Cancer Research

Pratikkumar Chunawala, Harshvardhan Chunawala

Chapter 2: Applying Reinforcement Learning to Optimize Cancer Treatment Protocols in Machine Learning Frameworks

Rohit R Dixit

Chapter 3: Extraction of Real-Time Data of Breast Cancer Patients and Implementation with ML Techniques

Mitanshi Rastogi, Meenu Vijarania, Neha Goel

Chapter 4: Decoding Images Convolutional Neural Networks in Oncological Medical Imaging

Deepak Rao Khadatkar

Chapter 5: Uncovering Insights in Cancer Research with Centroid-Based Clustering on Big Data

Shashi Kant Mishra, D. Srinivasu, Barre Praneeth Reddy, Rohit Jain

Chapter 6: The Role of Machine Learning in Remote Cancer Management: A Systematic Review

Rohit R Dixit

Chapter 7: Revolutionizing Cancer Drug Discovery Deep Learning Neural Networks for Accelerated Development             

Rupali Vyas, Rajendra Kumar Pandey, Dr. Huma Khan

Chapter 8: Empowering Patients Enhancing Engagement And Self-Care In Cancer Treatment With Bayesian Networks

Priyata Mishra

Chapter 9: Enhancing Cancer Detection and Classification with Ensemble Machine Learning Approaches

Dr. Suman Kumar Swarnkar, Rohit R Dixit

 

Chapter 10: Ethics, Regulation, and Machine Learning Navigating Oncological AI Deployment with Decision Trees

Keshika Jangde, Mrs. Preeti Tuli

Chapter 11: A Comprehensive Review of Big Data Integration and K-Means Clustering in Cancer Research

Harshvardhan Chunawala, Pratikkumar Chunawala

Chapter 12: Applications of Generative Adversarial Networks (GANs) in Healthcare

K.Deeba, D.Vathana, D.Vanusha, Ramaprabha.J

Chapter 13: Performance Analysis of Stochastic Gradient Descent and Adaptive Moment Estimation Optimization Algorithms for Convolutional Neural Networks

Sita Yadav, Dr. Sandeep Chaware

Chapter 14: Enhancing Oncology with Predictive Analytics for Cancer Diagnosis and Treatment with Random Forests

Shashi  Kant Mishra, Rajkumar Arugula, Pedhoori Rashmitha, Dr. K. Rajkumar

Chapter 15: Automated Diagnosis of Brain Tumors from MRI Scans Using U-Net Segmentation

Dr. Suman Kumar Swarnkar, Dr. Yogesh Kumar Rathore, Dr. Virendra Kumar Swarnkar