Brain Tumor Identification Based on VGG-16 Architecture and CLAHE Method
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 f...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Padang
2022-03-01
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| Series: | JOIV: International Journal on Informatics Visualization |
| Subjects: | |
| Online Access: | https://joiv.org/index.php/joiv/article/view/864 |
| _version_ | 1811159859038519296 |
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| author | Suci Aulia Dadi Rahmat |
| author_facet | Suci Aulia Dadi Rahmat |
| author_sort | Suci Aulia |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-04-10T05:47:56Z |
| format | Article |
| id | doaj.art-c530aef9a88e471db970edfbab96ebe5 |
| institution | Directory Open Access Journal |
| issn | 2549-9610 2549-9904 |
| language | English |
| last_indexed | 2024-04-10T05:47:56Z |
| publishDate | 2022-03-01 |
| publisher | Politeknik Negeri Padang |
| record_format | Article |
| series | JOIV: International Journal on Informatics Visualization |
| spelling | doaj.art-c530aef9a88e471db970edfbab96ebe52023-03-05T10:28:40ZengPoliteknik Negeri PadangJOIV: International Journal on Informatics Visualization2549-96102549-99042022-03-01619610210.30630/joiv.6.1.864325Brain Tumor Identification Based on VGG-16 Architecture and CLAHE MethodSuci Aulia0Dadi Rahmat1Telkom University, Bandung, IndonesiaBandung Institute of Technology, Bandung, IndonesiaMagnetic 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%.https://joiv.org/index.php/joiv/article/view/864brain tumormagnetic resonance imagingclahevgg-16deep learning. |
| spellingShingle | Suci Aulia Dadi Rahmat Brain Tumor Identification Based on VGG-16 Architecture and CLAHE Method JOIV: International Journal on Informatics Visualization brain tumor magnetic resonance imaging clahe vgg-16 deep learning. |
| title | Brain Tumor Identification Based on VGG-16 Architecture and CLAHE Method |
| title_full | Brain Tumor Identification Based on VGG-16 Architecture and CLAHE Method |
| title_fullStr | Brain Tumor Identification Based on VGG-16 Architecture and CLAHE Method |
| title_full_unstemmed | Brain Tumor Identification Based on VGG-16 Architecture and CLAHE Method |
| title_short | Brain Tumor Identification Based on VGG-16 Architecture and CLAHE Method |
| title_sort | brain tumor identification based on vgg 16 architecture and clahe method |
| topic | brain tumor magnetic resonance imaging clahe vgg-16 deep learning. |
| url | https://joiv.org/index.php/joiv/article/view/864 |
| work_keys_str_mv | AT suciaulia braintumoridentificationbasedonvgg16architectureandclahemethod AT dadirahmat braintumoridentificationbasedonvgg16architectureandclahemethod |