MRI-based Brain Tumor Analysis System | A Deep Learning Approach

Kalaiselvi Thiruvenkadam (u. a.)

ISBN 10: 620473718X / ISBN 13: 9786204737188

Published by LAP LAMBERT Academic Publishing, 2022

About this Item

MRI-based Brain Tumor Analysis System | A Deep Learning Approach | Kalaiselvi Thiruvenkadam (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786204737188 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 121200274

Bibliographic Details

• Title: MRI-based Brain Tumor Analysis System | A Deep Learning Approach
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2022
• Language: English
• ISBN 10: 620473718X
• ISBN 13: 9786204737188
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 4D millimeters 220 millimeters width by 150 millimeters height by 4 millimeters depth
• Weight: 102 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.