Advanced Machine Learning Approaches in Cancer Prognosis : Challenges and Applications
Author(s)
Bibliographic Information
Advanced Machine Learning Approaches in Cancer Prognosis : Challenges and Applications
（Intelligent systems reference library, v. 204）
Springer, c2021
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Other editors: Margarita N. Favorskaya, Seema Jain, Bighnaraj Naik, Manohar Mishra
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
Advances in Machine Learning Approaches in Cancer Prognosis.- Data Analysis on Cancer Disease using Machine Learning Techniques.- Learning from multiple modalities of imaging data for cancer detection/diagnosis .- Neural Network for Lung Cancer diagnosis.- Improved Thyroid Disease Prediction Model Using Data Mining Techniques with Outlier Detection.- Automated Breast Cancer Diagnosis Based on Neural Network Algorithms.
by "Nielsen BookData"