The International Arab Journal of Information Technology (IAJIT)


Neural Network with Bee Colony Optimization for MRI Brain Cancer Image Classification

 Brain tumor is one of the foremost causes for the i ncrease in mortality among children and adults. Com puter visions are being used by doctors to analysis and diagnose the medical problems. Magnetic Resonance Imaging (M RI) is a medical imaging technique, which is used to visualize inter nal structures of MRI brain images for analyzing no rmal and abnormal prototypes of brain while diagnosing. It is a non%i nvasive method to take picture of brain and the sur rounding images. Image processing techniques are used to extract meaningfu l information from medical images for the purpose o f diagnosis and prognosis. Raw MRI brain images are not suitable fo r processing and analysis since noise and low contrast affect the quality of the MRI images. The classification of MRI brain images is emphasized in this paper for cancer diagn osis. It can consist of four steps : Pre%processing, identification of Region of Intere st (ROI), feature extraction and classification. For improving quality of the image, partial differential equation s method is proposed and its result is compared wit h other methods such as block analysis method, opening by reconstruction me thod and histogram equalization method using statistical parameters such as carrier signal to ratio, peak signal%to%ratio, s tructural similarity index measure, figure of merit , mean square error. The enhanced image is converted into bi%level image, wh ich is utilized for sharpening the regions and filling the gaps in the binarized image using morphological operators ROI i s identified by applying region growing method for extorting the five features. The classification is performed based on the extracted image feature to determine whether th e brain image is normal or abnormal and it is also, introduced hybridizatio n of Neural Network (NN) with bee colony optimizati on for the classification and estimation of cancer affect on g iven MRI image. The performance of the proposed cla ssifier is compared with traditional NN classifier using statistical me asures such as sensitivity, specificity and accurac y. The experiment is conducted over 100 MRI brain images.

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