BRAIN TUMOR DETECTION USING DEEP LEARNING TECHNIQUES

Abstract

An automatic efficient system for brain tumor classification assists doctors in interpretation of medical images and supports decision of specialists in an early stages of tumor growth.Accurate and precise brain tumor MR images classification plays important role in clinical diagnosis and decision making for patient treatment.The need for a tumor detection program, thus, overcomes the lack of qualified radiologists.This process is difficult due to the wide variety of tumor tissues in the presence of different patients, and, in most cases, the similarity within normal tissues makes the

task difficult. The key challenge in MR images classification is the semantic gap between the low- level visual information captured by the MRI machine and the high-level information perceived

by the human evaluator. Processing of magnetic resonance images (MRI) is one among the parts of the image processing in medical field, which is the most emerging field from past few days. The tumor detection is often a preliminary phase.In the case study, Glioma, Meningioma,Pituitary tumor and No tumor types were classified using this method.The results reveal the effectiveness of the proposed method in classifying brain tumor via MRI images. EfficientNetB1 is the CNN architecture proposed here for the detection of brain tumor.