Smart and Sustainable Approaches for Optimizing Performance of Wireless Networks

6A Survey: Brain Tumor Detection Using MRI Image with Deep Learning Techniques

Chalapathiraju Kanumuri^1,2 and CH. Renu Madhavi¹

¹ RV College of Engineering, Bengaluru, Karnataka, India

² S.R.K.R Engineering College, Bhimavaram, Andhra Pradesh, India

6.1 Introduction

Brain tumors [1] form due to abnormal cell development [2]. They are tumors that occur in the skull or central portion of the spinal cord [3]. A chance of a tumor forming depends on a combination of components, including form, area, scale, and how it grows and propagates. Glioma is the primary cause of a brain tumor in adults. The tumors derive from glial cells and are classified into five grades. Grades I and II are considered low grade (LG), and grades III to V are high‐grade gliomas (HG) [4]. According to the American Association of Brain Tumors, in 2016, about 80 000 cases of brain tumor were reported [5]. Because of the complexity and size of clinical images, experts and radiologists are increasingly challenged in the time‐efficiency and precise analysis of these images. For the volume of interest (VOI) and region of interest (ROI) identification, an automatic method is therefore required to help explain medical images [6, 7]. Over the last decade, the tumor has become the deadliest perpetrator in the world [8]. Invasive methods for the identification, analysis, treatment, and simulation of tumors, have been replaced by better options such as radiographs, which include Magnetic Resonance Imaging (MRIs), Computed ...