Comparative study on Image Segmentation and Classification Analysis for brain Abnormality

Abstract

Brain abnormal is one of the most dangerous disease occurring commonly among human beings. There are many diseases such as Alzheimer’s Disease, Dementias, Epilepsy and other Seizure Disorders, Mental Disorders, etc. due to small abnormalities captured in MRI. The MRI brain abnormality segmentation is an important technique in medical diagnosis. Due to large variance and complexity of abnormal characteristics such as size, location, intensity and shape in MRI images, prediction of abnormal region is very complex. So currently manual tracing and delineating of segmentation of brain abnormality is in practice. The study of image segmentation and classification is done to improve the quality of image to train and classify different morphological functions. Accuracy is measured for the classification process and the classifier model can be found by comparing the accuracies obtained. It will be described the network building algorithm, chosen practical field for proposed method application and showed the results of its programming implementation.