Brain Tumor Classification Using Watershed Segmentation with ANN Classifier

Abstract

A brain tumor is an uncommon form of body cell proliferation. The most difficult tasks in the medical profession are to identify and categorize brain tumors. A person's life may be at risk if the brain tumor is not immediately identified or diagnosed. In this proposed method, an artificial neural network (ANN)-based technique can classify brain tumors accurately. Firstly, the images are normalized using the scaling process. Then the normalized images are segmented using the watershed algorithm. After that, the seven statistical features are extracted and then applied as input to the ANN classifier for the classification of the brain tumors. The experimental result of the proposed method provides an accuracy result of 95.8% which is better than modern state-of-the-art methods. Furthermore, compared to other contemporary techniques, the chosen seven statistical features are comparably few in illustrating this performance.