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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| 主要な著者: | , , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
2023
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| 主題: | |
| オンライン･アクセス: | http://eprints.uthm.edu.my/10554/1/J16367_86588b65d522261076f05e67e66ec666.pdf http://eprints.uthm.edu.my/10554/ https://doi.org/10.3991/ijoe.v19i08.38619 |
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| 要約: | 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. |
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