Brain Tumour Classification Into High Grade & Low-Grade Gliomas: A Comparitive Study

Abstract

With The Rapid Development In Bio Imaging Technology, Much Emphasis Has Been Placed On The Automation Of MRI-Based Brain Tumour Identification, Characterization, And Diagnostic Systems. The Most Common Form Of Primary Brain Tumour Is Gliomas. According To World Health Organization (WHO) Recommendations, They Are Divided Into Four Categories: Grade I, Grade II, Grade III, And Grade IV. The Precise Grading Of Gliomas Has Therapeutic Implications For Diagnosis, Surveillance, And Prognostic Procedures. The Primary Objective Of This Research Study Is To Compare And Evaluate The Diagnostic Efficiency Of Supervised And Unsupervised Learning-Based Classifiers In Recognizing The Difference Between High Grade Gliomas (Hggs) And Low Grade Gliomas (Lggs) By Extracting Histo-Pathological Features From MRI(Magnetic Resonance Imaging) Scanned Images. This Paper Explores Merits And Demerits Of Classification Algorithms Used For Grading In Recent Years. The Paper Also Highlights The Algorithms Used In Classification Stages Such As Preprocessing And Feature Extraction.