Brain Tumor Classification for MR Images Using Transfer Learning and EfficientNetB3

  • Ahmad Darman Huri Universitas Muhammadiyah Malang
  • Rizal Arya Suseno University of Muhammadiyah Malang
  • Yufis Azhar University of Muhammadiyah Malang
Keywords: Brain Tumor Classification, Convolutional Neural Network, EfficientNet, Transfer Learning

Abstract

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.

 

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Published
2022-12-29
How to Cite
Ahmad Darman Huri, Rizal Arya Suseno, & Yufis Azhar. (2022). Brain Tumor Classification for MR Images Using Transfer Learning and EfficientNetB3. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(6), 952 - 957. https://doi.org/10.29207/resti.v6i6.4357
Section
Artikel Teknologi Informasi

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