Brain Tumor Classification for MR Images Using Transfer Learning and
EfficientNetB3

Brain Tumor Classification for MR Images Using Transfer Learning and
EfficientNetB3

Brain Tumor Classification, Convolutional Neural Network, EfficientNet, Transfer learning

Brain tumors are one of the diseases that take many lives in the world, moreover, brain tumors have various types. In the
medical world, it has an technology called Magnetic Resonance Imaging (MRI) which functions to see the inside of the human
body using a magnetic field. CNN is designed to determine features adaptively using backpropagation by applying layers such
as convolutional layers, and pooling layers. This study aims to optimize and increase the accuracy of the classification of brain
tumor MRI images using the Convolutional Neural Network (CNN) EfficientNet model. The proposed system consists of two
main steps. First, preprocessing images using various methods then classifying images that have been preprocessed using
CNN. This study used 3064 images containing three types of brain tumors (gliomata, meningiomas, and pituitary). This study
resulted in an accuracy of 98.00%, a precision of 96.00%, and an average recall of 97.00% using the model that the researcher
applied.

Ahmad Darman Huri1
, Rizal Arya Suseno2
, Yufis Azhar3

University of Muhammadiyah Malang

29-12-2022

Fajar Bagus W

PDF

Indonesia

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