Investigation of VGG-16, ResNet-50 and AlexNet Performance for Brain Tumor Detection
Malignant brain tumours are extremely frequent and deadly, and if they are not found in their early stages, they can shorten a person’s lifespan. After the tumour has been detected, it is essential to classify the tumour in order to develop a successful treatment strategy. This study aims to invest...
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| Format: | Article |
| Language: | English |
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2023
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| Online Access: | http://eprints.uthm.edu.my/10554/1/J16367_86588b65d522261076f05e67e66ec666.pdf |
| _version_ | 1796870191055896576 |
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| author | Tun Azshafarrah Ton Komar Azaharan, Tun Azshafarrah Ton Komar Azaharan Abd Kadir Mahamad, Abd Kadir Mahamad Sharifah Saon, Sharifah Saon Muladi, Muladi Sri Wiwoho Mudjanarko, Sri Wiwoho Mudjanarko |
| author_facet | Tun Azshafarrah Ton Komar Azaharan, Tun Azshafarrah Ton Komar Azaharan Abd Kadir Mahamad, Abd Kadir Mahamad Sharifah Saon, Sharifah Saon Muladi, Muladi Sri Wiwoho Mudjanarko, Sri Wiwoho Mudjanarko |
| author_sort | Tun Azshafarrah Ton Komar Azaharan, Tun Azshafarrah Ton Komar Azaharan |
| collection | UTHM |
| description | Malignant brain tumours are extremely frequent and deadly, and if they are not found in their early stages, they can shorten a person’s lifespan. After the tumour has been detected, it is essential to classify the tumour in order
to develop a successful treatment strategy. This study aims to investigate the three deep learning tools, VGG-16 ResNet50 and AlexNet in order to detect brain tumor using MRI images. The results performance are then evaluated and compared using accuracy, precision and recall criteria. The dataset used contained 155 MRI images which are images with tumors, and 98 of them are nontumors. The AlexNet model perform extremely well on the dataset with 96.10% accuracy, while VGG-16 achieved 94.16% and ResNet-50 achieved 91.56%. The
early diagnosis of cancers before they develop physical side effects like paralysis and other problems is positively impacted by these accuracy. |
| first_indexed | 2024-03-05T22:05:52Z |
| format | Article |
| id | uthm.eprints-10554 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:05:52Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | uthm.eprints-105542024-01-03T01:36:34Z http://eprints.uthm.edu.my/10554/ Investigation of VGG-16, ResNet-50 and AlexNet Performance for Brain Tumor Detection Tun Azshafarrah Ton Komar Azaharan, Tun Azshafarrah Ton Komar Azaharan Abd Kadir Mahamad, Abd Kadir Mahamad Sharifah Saon, Sharifah Saon Muladi, Muladi Sri Wiwoho Mudjanarko, Sri Wiwoho Mudjanarko T Technology (General) Malignant brain tumours are extremely frequent and deadly, and if they are not found in their early stages, they can shorten a person’s lifespan. After the tumour has been detected, it is essential to classify the tumour in order to develop a successful treatment strategy. This study aims to investigate the three deep learning tools, VGG-16 ResNet50 and AlexNet in order to detect brain tumor using MRI images. The results performance are then evaluated and compared using accuracy, precision and recall criteria. The dataset used contained 155 MRI images which are images with tumors, and 98 of them are nontumors. The AlexNet model perform extremely well on the dataset with 96.10% accuracy, while VGG-16 achieved 94.16% and ResNet-50 achieved 91.56%. The early diagnosis of cancers before they develop physical side effects like paralysis and other problems is positively impacted by these accuracy. 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/10554/1/J16367_86588b65d522261076f05e67e66ec666.pdf Tun Azshafarrah Ton Komar Azaharan, Tun Azshafarrah Ton Komar Azaharan and Abd Kadir Mahamad, Abd Kadir Mahamad and Sharifah Saon, Sharifah Saon and Muladi, Muladi and Sri Wiwoho Mudjanarko, Sri Wiwoho Mudjanarko (2023) Investigation of VGG-16, ResNet-50 and AlexNet Performance for Brain Tumor Detection. Investigation of VGG-16, ResNet-50 and AlexNet Performance for Brain Tumor Detection, 19 (8). pp. 97-109. https://doi.org/10.3991/ijoe.v19i08.38619 |
| spellingShingle | T Technology (General) Tun Azshafarrah Ton Komar Azaharan, Tun Azshafarrah Ton Komar Azaharan Abd Kadir Mahamad, Abd Kadir Mahamad Sharifah Saon, Sharifah Saon Muladi, Muladi Sri Wiwoho Mudjanarko, Sri Wiwoho Mudjanarko Investigation of VGG-16, ResNet-50 and AlexNet Performance for Brain Tumor Detection |
| title | Investigation of VGG-16, ResNet-50 and AlexNet
Performance for Brain Tumor Detection |
| title_full | Investigation of VGG-16, ResNet-50 and AlexNet
Performance for Brain Tumor Detection |
| title_fullStr | Investigation of VGG-16, ResNet-50 and AlexNet
Performance for Brain Tumor Detection |
| title_full_unstemmed | Investigation of VGG-16, ResNet-50 and AlexNet
Performance for Brain Tumor Detection |
| title_short | Investigation of VGG-16, ResNet-50 and AlexNet
Performance for Brain Tumor Detection |
| title_sort | investigation of vgg 16 resnet 50 and alexnet performance for brain tumor detection |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/10554/1/J16367_86588b65d522261076f05e67e66ec666.pdf |
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