Medical Image Classification and Symptoms Detection Using Neuro Fuzzy - Mohd Basri, Mohd Ariffanan
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
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.