Medical Image Classification and Symptoms Detection Using Neuro Fuzzy | ANFIS Based Classification and Tumor Detection of The Brain Medical Image

Mohd Ariffanan Mohd Basri

ISBN 10: 3846583545 / ISBN 13: 9783846583548

Published by LAP LAMBERT Academic Publishing, 2012

About this Item

Medical Image Classification and Symptoms Detection Using Neuro Fuzzy | ANFIS Based Classification and Tumor Detection of The Brain Medical Image | Mohd Ariffanan Mohd Basri | Taschenbuch | 120 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783846583548 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 106637191

Bibliographic Details

• Title: Medical Image Classification and Symptoms Detection Using Neuro Fuzzy | ANFIS Based Classification and Tumor Detection of The Brain Medical Image
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3846583545
• ISBN 13: 9783846583548
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 8D millimeters 220 millimeters width by 150 millimeters height by 8 millimeters depth
• Weight: 197 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The conventional method in medicine for brain MR images classification and tumor detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. MR images also always contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use of artificial intelligent techniques, for instance, neural networks, fuzzy logic, neuro fuzzy have shown great potential in this field. Hence, in this project the neuro fuzzy system or ANFIS was applied for classification and detection purposes. Decision making was performed in two stages: feature extraction using the principal component analysis (PCA) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The performance of the ANFIS classifier was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS classifier has potential in detecting the tumors.

About the Author

Mohd Ariffanan Mohd Basri received the B.Eng. and the M.Eng. Degree in Mechatronics Engineering both from the Universiti Teknologi Malaysia in 2004 and 2009,respectively.He is currently a Tutor in the Department of Mechatronics Engineering of the Universiti Teknologi Malaysia.His research interests include Intelligent and Nonlinear Control Systems.