Classification of Malignant Tumors: A Practical Approach

Dipanjan Moitra

ISBN 10: 6139475007 / ISBN 13: 9786139475001

Published by LAP LAMBERT Academic Publishing, 2019

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - This book is mainly for the students of machine learning. This book also addresses the needs of the researchers who work in the knowledge field of bio-medical imaging and computer assisted oncology. This book demonstrates a holistic approach of malignant tumor classification via machine learning. It enumerates different stages of image analysis and image segmentation with the help of MATLAB code. WEKA data mining software has been used to describe both supervised and unsupervised learning methods. Each and every phase of tumor classification: feature extraction, data pre-processing, attribute selection, classification and model evaluation has been properly explained with the help of screenshots. It has also been depicted that, how the users may use python to execute such classification tasks.I hope this book will help the students, researchers as well as teachers working on machine learning as a ready reference.

Seller Inventory # 9786139475001

Bibliographic Details

• Title: Classification of Malignant Tumors: A Practical Approach
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 6139475007
• ISBN 13: 9786139475001
• 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

About this title

Synopsis

This book is mainly for the students of machine learning. This book also addresses the needs of the researchers who work in the knowledge field of bio-medical imaging and computer assisted oncology. This book demonstrates a holistic approach of malignant tumor classification via machine learning. It enumerates different stages of image analysis and image segmentation with the help of MATLAB code. WEKA data mining software has been used to describe both supervised and unsupervised learning methods. Each and every phase of tumor classification: feature extraction, data pre-processing, attribute selection, classification and model evaluation has been properly explained with the help of screenshots. It has also been depicted that, how the users may use python to execute such classification tasks.I hope this book will help the students, researchers as well as teachers working on machine learning as a ready reference.