Artificial Intelligence in Cancer (eBook)

Detection, Diagnosis, and Treatment
Artikelnummer: 978-3-11-167151-2
Einband: Adobe Digital Editions
Verfügbarkeit: Download, sofort verfügbar (Link per E-Mail)
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This book explores a wide range of methods for advanced solutions in the field of oncology. It explores the transformative role of AI in advancing cancer care, focusing on its applications in drug discovery, surgical progress, and digital twin technology. The book provides an in-depth analysis of AI's impact on various cancers, including breast, colorectal, brain, and others, and highlights its potential to enhance early detection, accurate diagnosis, and personalized treatment. The book will investigate the development and optimization of AI algorithms to achieve high accuracy in detecting malignant cells at different stages. The technologies encompass machine learning techniques to identify patterns in medical imaging, natural language processing to evaluate patient histories, and deep learning models to forecast treatment outcomes. Specific methodologies such as supervised and unsupervised learning models, convolutional neural networks for image analysis, and reinforcement learning for adaptive treatment strategies are provided. The utilization of case studies and real-world examples in cancer care will demonstrate every approach, enabling readers to gain a practical understanding of how these technologies might be implemented.

This book explores a wide range of methods for advanced solutions in the field of oncology. It explores the transformative role of AI in advancing cancer care, focusing on its applications in drug discovery, surgical progress, and digital twin technology. The book provides an in-depth analysis of AI's impact on various cancers, including breast, colorectal, brain, and others, and highlights its potential to enhance early detection, accurate diagnosis, and personalized treatment. The book will investigate the development and optimization of AI algorithms to achieve high accuracy in detecting malignant cells at different stages. The technologies encompass machine learning techniques to identify patterns in medical imaging, natural language processing to evaluate patient histories, and deep learning models to forecast treatment outcomes. Specific methodologies such as supervised and unsupervised learning models, convolutional neural networks for image analysis, and reinforcement learning for adaptive treatment strategies are provided. The utilization of case studies and real-world examples in cancer care will demonstrate every approach, enabling readers to gain a practical understanding of how these technologies might be implemented.

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VerlagDe Gruyter
EinbandAdobe Digital Editions
Erscheinungsjahr2026
Seitenangabe308 S.
AusgabekennzeichenEnglisch
Abbildungen32 b/w and 33 col. ill.
Masse3'777 KB
Auflage26001 A. 1. Auflage
PlattformEPUB
ReiheDe Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences
AutorMalviya, Rishabha / Rajput, Shivam / Roy, Mukesh / Sridhar, Sathvik Belagodu / Thomas, Sabin

Alle Bände der Reihe "De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences"

Über den Autor Rishabha Malviya

261910738

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