mri based brain tumor analysis di thiruvenkadam kalaiselvi (4 risultati)

Taschenbuch. Condizione: Neu. Neuware -This book is the outcome of our research practice over 15 years in the area of brain tumor image analysis using computational intelligence techniques. Many automatic methods were developed and published in reputed international journals. Currently, deep learning-based models are targeted to classify brain tumors from magnetic resonance imaging (MRI) human head scans. This book is primarily focused on the development of an MRI-based brain tumor analysis system using a deep learning approach. This book is organized into five chapters. Chapter 1 describes the significance of medical image processing, the principles and role of MRI in medical image analysis and the importance of artificial intelligence (AI) systems. Chapter 2 explains the anatomy of the brain, the cause of brain tumor and its types. Chapter 3 describes the rudiments of deep learning (DL) techniques such as machine learning (ML) basics, algorithms, evolution, role in tumor imaging, the architecture of deep neural networks (DNN) and convolutional neural networks (CNN). Chapter 4 explains the development of the proposed DL tool of brain tumor analysis. Finally, chapter 5 concludes with further works in future.Books on Demand GmbH, Überseering 33, 22297 Hamburg 56 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book is the outcome of our research practice over 15 years in the area of brain tumor image analysis using computational intelligence techniques. Many automatic methods were developed and published in reputed international journals. Currently, deep lea.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is the outcome of our research practice over 15 years in the area of brain tumor image analysis using computational intelligence techniques. Many automatic methods were developed and published in reputed international journals. Currently, deep learning-based models are targeted to classify brain tumors from magnetic resonance imaging (MRI) human head scans. This book is primarily focused on the development of an MRI-based brain tumor analysis system using a deep learning approach. This book is organized into five chapters. Chapter 1 describes the significance of medical image processing, the principles and role of MRI in medical image analysis and the importance of artificial intelligence (AI) systems. Chapter 2 explains the anatomy of the brain, the cause of brain tumor and its types. Chapter 3 describes the rudiments of deep learning (DL) techniques such as machine learning (ML) basics, algorithms, evolution, role in tumor imaging, the architecture of deep neural networks (DNN) and convolutional neural networks (CNN). Chapter 4 explains the development of the proposed DL tool of brain tumor analysis. Finally, chapter 5 concludes with further works in future. 56 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is the outcome of our research practice over 15 years in the area of brain tumor image analysis using computational intelligence techniques. Many automatic methods were developed and published in reputed international journals. Currently, deep learning-based models are targeted to classify brain tumors from magnetic resonance imaging (MRI) human head scans. This book is primarily focused on the development of an MRI-based brain tumor analysis system using a deep learning approach. This book is organized into five chapters. Chapter 1 describes the significance of medical image processing, the principles and role of MRI in medical image analysis and the importance of artificial intelligence (AI) systems. Chapter 2 explains the anatomy of the brain, the cause of brain tumor and its types. Chapter 3 describes the rudiments of deep learning (DL) techniques such as machine learning (ML) basics, algorithms, evolution, role in tumor imaging, the architecture of deep neural networks (DNN) and convolutional neural networks (CNN). Chapter 4 explains the development of the proposed DL tool of brain tumor analysis. Finally, chapter 5 concludes with further works in future.