Advanced Machine Learning Approaches in Cancer Prognosis
Produktnummer:
18cf8f99b324f44a6e83f51739861fec80
Themengebiete: | Cancer Cancer Reserch Evolutionary Approach Machine Learning Neural Network Neuro-Evolutionary Approach Neuro-Fuzzy Method Rough Set Theory Soft Computing |
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Veröffentlichungsdatum: | 30.05.2021 |
EAN: | 9783030719746 |
Sprache: | Englisch |
Seitenzahl: | 454 |
Produktart: | Gebunden |
Herausgeber: | Favorskaya, Margarita N. Jain, Seema Mishra, Manohar Naik, Bighnaraj Nayak, Janmenjoy |
Verlag: | Springer International Publishing |
Untertitel: | Challenges and Applications |
Produktinformationen "Advanced Machine Learning Approaches in Cancer Prognosis"
This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid intelligent systems for the determination and diagnosis of cancer. Moreover, the tactical solutions of machine learning have proved its vast range of significance and, provided novel solutions in the medical field for the diagnosis of disease. This book also explores the distinct deep learning approaches that are capable of yielding more accurate outcomes for the diagnosis of cancer. In addition to providing an overview of the emerging machine and deep learning approaches, it also enlightens an insight on how to evaluate the efficiency and appropriateness of such techniques and analysis of cancer data used in the cancer diagnosis. Therefore, this book focuses on the recent advancements in the machine learning and deep learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted audience including scientists, experts, Ph.D. students, postdocs, and anyone interested in the subjects discussed.

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