Brain Tumor Identification Based on VGG-16 Architecture and CLAHE Method

Abstract

Magnetic Resonance Imaging (MRI) in diagnosing brain cancers is widespread. Because of the variety of angles and clarity of anatomy, it is commonly employed. If a brain tumor is malignant or secondary, it is a high risk, leading to death. These tumors have an increased predisposition for spreading from one place to another. In detecting brain abnormality form such as a tumor, from a magnetic resonance scan, expertise and human involvement are required. Previous, the image segmentation of brain tumors is widely developed in this field. Suppose we could somehow use an automatic brain tumor detection technology to identify the presence of a tumor in the brain without requiring human intervention. In that case, it will give us a leg up in the treatment process. This research proposed two stages to identify the brain tumor in MRI; the first stage was the image enhancement process using Clip Limit Adaptive Histogram Equalization (CLAHE) to segment the brain MRI. The second one was classifying the brain tumor on MRI using Visual Geometry Group-16 Layer (VGG-16). The CLAHE was used in some instances, there were CLAHE applied in FLAIR image on green color, and CLAHE applied in Red, Green, Blue (RGB) color space. The experimental result showed the highest performance with accuracy, precision, recall, respectively 90.37%, 90.22%, 87.61%. The CLAHE method in RGB Channel and the VGG-16 model have reliably on predicted oligodendroglioma classes in RGB enhancement with precision 91.08% and recall 95.97%.

Keywords

• brain tumor
• magnetic resonance imaging
• clahe
• vgg-16
• deep learning.