BRAIN TUMOR SEGMENTATION AND CLASSIFICATION USING OPTIMIZED U-NET

Abstract

In the brain, the abnormal growth of cells or solid intracranial neoplasm is known as brain tumor, which is one of the world’s most tedious diseases. Hence, there is a need for segmentation and classification of the brain tumor accurately. It is difficult to separate the tumor tissues and other tissues from the brain. The major aim of this research is to use magnetic resonance imaging (MRI) segment and classify the brain tumor and all the abnormalities in the brain. The MRI is initially fed into the pre-processing system and then it is segmented using the region-growing segmentation algorithm in the pre-operative MRI. It produces the segmented area and it is forwarded for classification. In the classification step, the Honey Badger Algorithm (HBA) is applied to train the U-Net classifier. The tumor tissues and the different types of tissues or abnormalities in brain tumors are classified by this algorithm. Overall, the post-operative and pre-operative MRI brain tumor segmentation and classification consist of the same steps. To find out the pixel changes, both the segmented output of pre-operative and post-operative MRI was compared. It helps in finding the emerging tumor after surgery and the success rate of surgery. Based on pre-operative MRI, the implemented scheme has maximum specificity, sensitivity, and accuracy of 0.977, 0.968, and 0.949.