Neural network with bee colony optimization for MRI brain cancer image classification
B rain tumor is one of the foremost causes for the increase in mortality among children and adults. Computer visions are being used by doctors to analysis and diagnose the medical problems. Magnetic Resonance Imaging (MRI) is a medical imaging technique, which is used to visualize internal structures of MRI brain images for analyzing normal and abnormal prototypes of brain while diagnosing. It is a non"invasive method to take picture of brain and the surrounding images. Image processing techniques are used to extract meaningful information from medical images for the purpose of diagnosis and prognosis. Raw MRI brain images are not suitable for 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 diagnosis. It can consist of four steps: Pre"processing, identification of region of interest, feature extraction and classification. For improving quality of the image, partial differential equations method is proposed and its result is compared with other methods such as block analysis method, opening by reconstruction method and histogram equalization method using statistical parameters such as carrier signal to ratio, peak signal"to"ratio, structural similarity index measure, figure of merit, mean square error. The enhanced image is converted into bi"level image, which is utilized for sharpening the regions and filling the gaps in the binarized image using morphological operators. Region of Interest (ROI) is identified by applying region growing method for extorting the five features. The classification is performed based on the extracted image feature to determine whether the brain image is normal or abnormal and it is also, introduced hybridization of Neural Network (NN) with bee colony optimization for the classification and estimation of cancer affect on given MRI image. The performance of the proposed classifier is compared with traditional NN classifier using statistical measures such as sensitivity, specificity and accuracy. The experiment is conducted over 100 MRI brain images.
Int. Arab J. Inf. Technol.