Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning

Krisna Nuresa Qodri; Indah Soesanti; Hanung Adi Nugroho

doi:10.22146/ijitee.62663

Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning

https://doi.org/10.22146/ijitee.62663

Krisna Nuresa Qodri^(1*), Indah Soesanti⁽²⁾, Hanung Adi Nugroho⁽³⁾

(1) Universitas Gadjah Mada
(2) Universitas Gadjah Mada
(3) Universitas Gadjah Mada
(*) Corresponding Author

Abstract

Tumors are cells that grow abnormally and uncontrollably, whereas brain tumors are abnormally growing cells growing in or near the brain. It is estimated that 23,890 adults (13,590 males and 10,300 females) in the United States and 3,540 children under the age of 15 would be diagnosed with a brain tumor. Meanwhile, there are over 250 cases in Indonesia of patients afflicted with brain tumors, both adults and infants. The doctor or medical personnel usually conducted a radiological test that commonly performed using magnetic resonance image (MRI) to identify the brain tumor. From several studies, each researcher claims that the results of their proposed method can detect brain tumors with high accuracy; however, there are still flaws in their methods. This paper will discuss the classification of MRI-based brain tumors using deep learning and transfer learning. Transfer learning allows for various domains, functions, and distributions used in training and research. This research used a public dataset. The dataset comprises 253 images, divided into 98 tumor-free brain images and 155 tumor images. Residual Network (ResNet), Neural Architecture Search Network (NASNet), Xception, DenseNet, and Visual Geometry Group (VGG) are the techniques that will use in this paper. The results got to show that the ResNet50 model gets 96% for the accuracy, and VGG16 gets 96% for the accuracy. The results obtained indicate that transfer learning can handle medical images.

Keywords

Tumor;Brain Tumors;Magnetic Resonance Image (MRI);Accuracy;Deep Learning;Transfer Learning;VGG16;ResNet50

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DOI: https://doi.org/10.22146/ijitee.62663