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.
