Penerapan Deep Learning Menggunakan VGG-16 untuk Klasifikasi Citra Glioma
 (*)Annisa Putri  (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
 Benny Sukma Negara (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
Suwanto Sanjaya (Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia)
(*) Corresponding Author Submitted: May 13, 2022; Published: June 30, 2022 |
Abstract
One of the types of brain tumors in humans is glioma. Glioma is considered to be the most common type of primary brain tumor in adults. To determine the follow-up action that will be carried out by the doctor, the level of glioma needs to be known first. Glioma is divided into 3 grades. To be able to distinguish grades from gliomas, a classification process can be carried out using deep learning with CNN architecture. Glioma grade classification applies Histogram Equalization (HE) preprocessing. The training model uses CNN with the VGG-16 architecture. data using split data with a comparison of 70% training 30% testing, 80% training 20% testing, and 90% training 10% testing. The results of this study using original data have better results compared to data using HE preprocessing on batch size 16 testing and split data 90% training 10% testing.
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