Classification of Malignant Tumors: A Practical Approach
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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 ...
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.
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