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Medical image classification and symptoms detection using neuro fuzzy

Mohd. Basri, Mohd. Ariffanan (2008) Medical image classification and symptoms detection using neuro fuzzy. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering.

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Abstract

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.

Item Type:Thesis (Masters)
Additional Information:Thesis (Sarjana Kejuruteraan (Elektrik - Mekatronik dan Kawalan Automatik)) - Universiti Teknologi Malaysia, 2008; Supervisor : Dr. Hj. Mohd. Fauzi Othman
Uncontrolled Keywords:brain MR images, tumor detection, neuro fuzzy system
Subjects:R Medicine > RZ Other systems of medicine
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QA Mathematics > QA76 Computer software
Divisions:Electrical Engineering
ID Code:9503
Deposited By: Ms Zalinda Shuratman
Deposited On:31 Dec 2009 08:55
Last Modified:07 Aug 2012 06:47

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