Klasifikasi Tumor Otak pada Citra Magnetic Resonance Image dengan Menggunakan Metode Support Vector Machine

Febrianti, Ainani Shabrina; Sardjono, Tri Arief; Babgei, Atar Fuady

doi:10.12962/j23373539.v9i1.51587

Cited by 10 publications

(12 citation statements)

References 3 publications

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“…Jika dibandingkan pada penelitian yang dilakukan oleh Febrianti dkk. [6] kontribusi yang kami tambahkan adalah menggunakan metode pembelajaran mesin yang berbeda, mempersingkat tahapan praproses, serta hasil pengujian yang kami dapatkan juga lebih baik.…”

Section: Pendahuluanunclassified

“…[7] Data set [5] yang penulis gunakan juga pernah digunakan oleh Febrianti dkk. dalam penelitiannya [6] di mana metode pembelajaran mesin yang mereka gunakan adalah Support Vector Machine (SVM). Sebelum masuk ke tahap klasifikasi oleh SVM data set yang berupa citra terlebih dahulu dipraproses adapun metode praproses yang digunakan antara lain Gaussian blur, binary thresholding, dilatasi dan erosi, mencari kontur terbesar, extreme point pada kontur, dan melakukan pemangkasan citra otomatis.…”

Section: Penelitian Terdahuluunclassified

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Implementasi Metode Extreme Learning Machine untuk Klasifikasi Tumor Otak pada Citra Magnetic Resonance Imaging

Wahid

Anggraeny

Nugroho

2020

santika

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Tumor otak merupakan penyakit yang menyerang otak makhluk hidup di mana sel otak tumbuh secara tidak normal pada daerah sekitar otak. Berbagai macam cara telah dilakukan untuk mendeteksi penyakit ini, bahkan dengan bantuan teknologi digital, satunya dengan melalui pendekatan anatomi citra kesehatan. Pada penelitian ini kami mengusulkan metode Extreme Learning Machine (ELM) sebagai pengklasifikasi citra tumor otak yang didapat melalui Magnetic Resonance Imaging. ELM dipilih karena keunggulannya pada proses pelatihan, yaitu lebih cepat dibanding algoritma pembelajaran mesin yang sifatnya iteratif. Dataset yang kami gunakan adalah Brain MRI Images for Tumor Detection. Dataset tersebut berisi dua kategori citra yaitu Tumor dan Otak tanpa tumor (sehat). Citra pada dataset tersebut dipraproses sebelum masuk tahap klasifikasi, ELM dilatih pada data latih dan melakukan pengujian performa pada data uji. Hasilnya ELM yang memiliki jumlah node pada lapisan tersembunyi sebanyak 2500 memiliki performa yang palin baik dibandingkan ELM yang memiliki node pada lapisan tersembunyi sebanyak 3000, 3500, dan 4000. Ini dibuktikan melalui nilai rata-rata presisi, recall, dan F1-score nya adalah 0.86 dan akurasi ujinya sebesar 86%.

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Section: Pendahuluanunclassified

Section: Penelitian Terdahuluunclassified

Implementasi Metode Extreme Learning Machine untuk Klasifikasi Tumor Otak pada Citra Magnetic Resonance Imaging

Wahid

Anggraeny

Nugroho

2020

santika

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“…Meanwhile, a minority of other medical personnel use manual diagnosis. The technology in question is Magnetic Resonance Imaging (MRI) image examination [8]. MRI is a set of scans using a magnetic field and a computer to capture images of the brain on film [9].…”

Section: Introductionmentioning

confidence: 99%

Classification of Brain Tumors on MRI Images Using Convolutional Neural Network Model EfficientNet

Wibowo

Fayyadl

Azhar

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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A brain tumor is a lump caused by an imperfect cell turnover cycle in the brain and can affect all ages. Brain tumors have 4 grades, namely grades 1 to 2 are benign tumor grades, and grades 3 to 4 are malignant tumor grades. Therefore, early identification of brain tumor disease is very important in providing appropriate treatment and treatment. This study uses a dataset obtained through the Kaggle website titled Brain Tumor Classification (MRI). The number of data is 3264 images with details of Glioma tumors (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and without tumors (500 images). In this study, there are 4 scenarios with different testers. This study proposes the classification of brain tumors using Hyperparameter Tuning and EfficientNet models on MRI images. The EfficientNet model used is the EfficientNetB0 and EfficientNetB7 models with the architecture used are the input layer, GlobalAveragePooling2D layer, dropout layer, and dense layer as well as adding augmentation data to the dataset to manipulate the data in order to improve the results of the proposed model. After building the model, the results of accuracy, precision, recall, and f1-score will be obtained in each scenario. Accuracy results in Scenario 1 are 91%, scenario 2 is 95% accurate, scenario 3 is 95%, and scenario 4 is 98%.

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“…According to reports, about 23,890 adults (13,590 males and 10,300 females) in the United States and nearly 3,540 children under the age of 15 are diagnosed with brain tumors [3]. Meanwhile, there are over 250 cases of patients afflicted with brain tumors, both adults and infants, in Indonesia [4].…”

Section: Introductionmentioning

confidence: 99%

“…Radiological examination using magnetic resonance imaging (MRI) is needed to determine whether a person has a tumor or not. MRI is selected because it can observe a distinction of soft tissues such as white matter and gray matter [4]. The MRI analysis will create a picture that represents the brain.…”

Section: Introductionmentioning

confidence: 99%

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

Qodri

Soesanti

Nugroho

2021

IJITEE

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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.

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Klasifikasi Tumor Otak pada Citra Magnetic Resonance Image dengan Menggunakan Metode Support Vector Machine

Cited by 10 publications

References 3 publications

Implementasi Metode Extreme Learning Machine untuk Klasifikasi Tumor Otak pada Citra Magnetic Resonance Imaging

Implementasi Metode Extreme Learning Machine untuk Klasifikasi Tumor Otak pada Citra Magnetic Resonance Imaging

Classification of Brain Tumors on MRI Images Using Convolutional Neural Network Model EfficientNet

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

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