Abstract— Hydrocephalus is considered to be one of the diseases that may cause damage in children brain especially infants. MRI (Magnetic resonance Imaging) is one source of hydrocephalus detection tools, but using MRI inchildren brain diseases classification is considered to be difficult process according to the variance and complexity ofbrain diseases. This paper presents a solution of detecting one of the children brain diseases which is hydrocephalus. The proposed system consists of four stages, namely, MRI Preprocessing stage, Segmentation stage, Featureextraction, and Classification stage. In the first stage, the main task is to eliminate the medical resonance images(MRI) noise found in images due to light reflections or operator performance which may cause inaccuracies in theclassification process. The second stage, which is the stage where ROI is extracted (tumor region). In the third stage, the features related with MRI images using Haar wavelet transform (HWT) will be obtained. The features of magneticresonance images (MRI) have been decreased using (HWT) to essential features only. And finally the fourth stages,where new classifier will be presented and finally the result will compare the proposed classifier with six other classifiers have been used.Image classification is an important task in the medical field and computer vision. Image classification refers to the process of labeling images into one of a number of predefined categories. This survey will use the Tree augmentedNaïve Bayes classification technique to detect and classify one of the children brain diseases, and classify the hydrocephalus type depending on MRI. And it's expected to achieve a high accuracy in hydrocephalus detection to help the radiologist in the disease detection process.
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