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All Journal JOIV : International Journal on Informatics Visualization
Zaidah Ibrahim
Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
Computer Science & IT

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Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine Agus Eko Minarno; Ilham Setiyo Kantomo; Fauzi Dwi Setiawan Sumadi; Hanung Adi Nugroho; Zaidah Ibrahim
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.991

Abstract

The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.
Convolutional Neural Network featuring VGG-16 Model for Glioma Classification Agus Eko Minarno; Sasongko Yoni Bagas; Munarko Yuda; Nugroho Adi Hanung; Zaidah Ibrahim
JOIV : International Journal on Informatics Visualization Vol 6, No 3 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.3.1230

Abstract

Magnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%. 
Co-Authors Agus Eko Minarno Agus Eko Minarno Hanung Adi Nugroho Ilham Setiyo Kantomo Munarko Yuda Nugroho Adi Hanung Sasongko Yoni Bagas Sumadi, Fauzi Dwi Setiawan