EFFECTIVE ALGORITHM FOR BENIGN BRAIN TUMOR DETECTION USING FRACTIONAL CALCULUS

Brain tumor detection has gained research interest due to complexity involved in tumor detection and mortality ratio over the few decades. The present manuscript primarily addresses issues regarding benign brain tumor detection. Brain tumor detection involves segmentation in primary stage. Segmentation using existing techniques have some limitation like unable to handle noisy data and unable to detect small intensity variation in the image. In this manuscript, a new fractional mask design for benign brain tumor detection is proposed. Qualitative and quantitative analysis have been performed to prove superiority over existing boundary based methods available .

1. INTRODUCTION

Cancer is one of the major cause of death among children and adults around the world [1]. A survey is being conducted by National Cancer Institute and 10% growth rate has been found each year in cases related to cancer. Recent studies shows an increase of 300 per year in the cases related to brain tumor [2]. Categorization of brain tumor uses grade I to grade IV to classify benign and malignant brain tumors [3]. Grade I and II (benign tumors) have slow growth rate and if left untreated, is likely to develop into grade III and IV (malignant brain tumor). There is a very small intensity variation in tissues of grade I/II tumor and healthy tissues.