Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI Images
Brain tumor classification using MRI images is a significant challenge in medical diagnosis, requiring models with high accuracy and efficient training. This study aims to compare the performance of three Convolutional Neural Network (CNN) models—ResNet50, VGG16, and MobileNetV2—for brain tumor clas...
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| Format: | Article |
| Language: | Indonesian |
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Islamic University of Indragiri
2025-03-01
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| Series: | Sistemasi: Jurnal Sistem Informasi |
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| Online Access: | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5054 |
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| author | Muhammad Bayu Kurniawan Ema Utami |
| author_facet | Muhammad Bayu Kurniawan Ema Utami |
| author_sort | Muhammad Bayu Kurniawan |
| collection | DOAJ |
| description | Brain tumor classification using MRI images is a significant challenge in medical diagnosis, requiring models with high accuracy and efficient training. This study aims to compare the performance of three Convolutional Neural Network (CNN) models—ResNet50, VGG16, and MobileNetV2—for brain tumor classification based on MRI images. The dataset consists of four brain tumor categories: glioma, meningioma, pituitary, and no tumor, with data split into training, validation, and testing sets. Each model was evaluated using metrics including accuracy, precision, recall, F1-score, specificity, and training time to assess their effectiveness in predicting brain tumors with optimal accuracy and efficiency. Experimental results indicate that VGG16 achieved the best overall performance, with an accuracy of 94.93%, precision of 94.68%, and specificity of 98.33%, while also having the shortest training time of 47.15 minutes. MobileNetV2 demonstrated strong performance with a recall of 94.08% but required a longer training time of 79.53 minutes. ResNet50 recorded the lowest accuracy (91.67%) despite excelling in precision (91.79%), but it underperformed in recall (91.25%) and specificity (97.2%). Overall, this study confirms that VGG16 is the most efficient and effective model for MRI-based brain tumor classification. |
| format | Article |
| id | doaj-art-e143359f008f419c961f54eec3fbb2ed |
| institution | OA Journals |
| issn | 2302-8149 2540-9719 |
| language | Indonesian |
| publishDate | 2025-03-01 |
| publisher | Islamic University of Indragiri |
| record_format | Article |
| series | Sistemasi: Jurnal Sistem Informasi |
| spelling | doaj-art-e143359f008f419c961f54eec3fbb2ed2025-08-20T01:55:11ZindIslamic University of IndragiriSistemasi: Jurnal Sistem Informasi2302-81492540-97192025-03-0114276777710.32520/stmsi.v14i2.50541039Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI ImagesMuhammad Bayu Kurniawan0Ema Utami1Universitas Amikom YogyakartaUniversitas Amikom YogyakartaBrain tumor classification using MRI images is a significant challenge in medical diagnosis, requiring models with high accuracy and efficient training. This study aims to compare the performance of three Convolutional Neural Network (CNN) models—ResNet50, VGG16, and MobileNetV2—for brain tumor classification based on MRI images. The dataset consists of four brain tumor categories: glioma, meningioma, pituitary, and no tumor, with data split into training, validation, and testing sets. Each model was evaluated using metrics including accuracy, precision, recall, F1-score, specificity, and training time to assess their effectiveness in predicting brain tumors with optimal accuracy and efficiency. Experimental results indicate that VGG16 achieved the best overall performance, with an accuracy of 94.93%, precision of 94.68%, and specificity of 98.33%, while also having the shortest training time of 47.15 minutes. MobileNetV2 demonstrated strong performance with a recall of 94.08% but required a longer training time of 79.53 minutes. ResNet50 recorded the lowest accuracy (91.67%) despite excelling in precision (91.79%), but it underperformed in recall (91.25%) and specificity (97.2%). Overall, this study confirms that VGG16 is the most efficient and effective model for MRI-based brain tumor classification.https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5054resnet50vgg16mobilenetv2brain tumor classificationmri image |
| spellingShingle | Muhammad Bayu Kurniawan Ema Utami Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI Images Sistemasi: Jurnal Sistem Informasi resnet50 vgg16 mobilenetv2 brain tumor classification mri image |
| title | Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI Images |
| title_full | Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI Images |
| title_fullStr | Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI Images |
| title_full_unstemmed | Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI Images |
| title_short | Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI Images |
| title_sort | performance comparison of resnet50 vgg16 and mobilenetv2 for brain tumor classification on mri images |
| topic | resnet50 vgg16 mobilenetv2 brain tumor classification mri image |
| url | https://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/5054 |
| work_keys_str_mv | AT muhammadbayukurniawan performancecomparisonofresnet50vgg16andmobilenetv2forbraintumorclassificationonmriimages AT emautami performancecomparisonofresnet50vgg16andmobilenetv2forbraintumorclassificationonmriimages |