International Journal of Computer Science Issues

More than a traditional journal...

Medical image data like ECG, EEG and MRI, CT-scan images are the most important way to diagnose disease of human being in precise way and widely used by the physician. Problem can be clearly identified with the help of these medical images. A robust model can classify the medical image data in better way .In this paper intelligent techniques like neural network and fuzzy logic techniques are explored for MRI medical image data to identify tumor in human brain. Also need of preprocessing of medical image data is explored. Classification technique has been used extensively in the field of medical imaging. The conventional method in medical science for medical image data classification is done by human inspection which may result misclassification of data sometime this type of problem identification are impractical for large amounts of data and noisy data, a noisy data may be produced due to some technical fault of the machine or by human errors and can lead misclassification of medical image data. We have collected number of papers based on neural network and fuzzy logic along with hybrid technique to explore the efficiency and robustness of the model for brain MRI data. It has been analyzed that intelligent model along with data preprocessing using principal component analysis (PCA) and segmentation may be the competitive model in this domain.