Convolutional Neural Network featuring VGG-16 Model for Glioma Classification

Agus Minarno - Universitas Muhammadiyah Malang, Malang, Indonesia
Sasongko Bagas - Universitas Muhammadiyah Malang, Malang, Indonesia
Munarko Yuda - Universitas Muhammadiyah Malang, Malang, Indonesia
Nugroho Hanung - Universitas Gadjah Mada, Yogyakarta, Indonesia
Zaidah Ibrahim - Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

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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%. 


Classification; MRI; brain tumor; Glioma, CNN; VGG-16.

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