Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data
Deep learning models have become essential for automated medical image analysis in brain tumor detection. Existing Convolutional Neural Network (CNN) models like Visual Geometry Group 19 (VGG19), Residual Network 18 (ResNet18), and Residual Network 34 (ResNet34), despite their success in image class...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03014.pdf |
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| _version_ | 1825206587943288832 |
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| author | Zhu Zhimeng |
| author_facet | Zhu Zhimeng |
| author_sort | Zhu Zhimeng |
| collection | DOAJ |
| description | Deep learning models have become essential for automated medical image analysis in brain tumor detection. Existing Convolutional Neural Network (CNN) models like Visual Geometry Group 19 (VGG19), Residual Network 18 (ResNet18), and Residual Network 34 (ResNet34), despite their success in image classification and recognition, face challenges such as unclear boundary detection, limited generalization, and lower computational efficiency in detecting brain tumors. To address these issues, this study introduces VGG19-Brain’s MRI for Tumor (BMT), an enhanced version of the classic VGG19 model. VGG19-BMT incorporates targeted optimizations, including adjustments to convolutional layers and improved feature extraction modules. A systematic comparative analysis of VGG19-BMT and traditional CNN models was conducted using the Kaggle dataset “Brain MRI Images for Brain Tumor Detection.” The results demonstrate that VGG19-BMT outperforms conventional models’ boundary recognition accuracy, generalization, and computational efficiency, providing a more effective solution for automated brain tumor detection. This advancement not only enhances diagnostic capabilities but also sets the stage for future model improvements and clinical applications in medical imaging. |
| format | Article |
| id | doaj-art-b5bcbe7997f54ea3a57ed9bbb11bd4bb |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-b5bcbe7997f54ea3a57ed9bbb11bd4bb2025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700301410.1051/itmconf/20257003014itmconf_dai2024_03014Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI DataZhu Zhimeng0School of Artificial Intelligence and Automation, Huazhong University of Science and TechnologyDeep learning models have become essential for automated medical image analysis in brain tumor detection. Existing Convolutional Neural Network (CNN) models like Visual Geometry Group 19 (VGG19), Residual Network 18 (ResNet18), and Residual Network 34 (ResNet34), despite their success in image classification and recognition, face challenges such as unclear boundary detection, limited generalization, and lower computational efficiency in detecting brain tumors. To address these issues, this study introduces VGG19-Brain’s MRI for Tumor (BMT), an enhanced version of the classic VGG19 model. VGG19-BMT incorporates targeted optimizations, including adjustments to convolutional layers and improved feature extraction modules. A systematic comparative analysis of VGG19-BMT and traditional CNN models was conducted using the Kaggle dataset “Brain MRI Images for Brain Tumor Detection.” The results demonstrate that VGG19-BMT outperforms conventional models’ boundary recognition accuracy, generalization, and computational efficiency, providing a more effective solution for automated brain tumor detection. This advancement not only enhances diagnostic capabilities but also sets the stage for future model improvements and clinical applications in medical imaging.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03014.pdf |
| spellingShingle | Zhu Zhimeng Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data ITM Web of Conferences |
| title | Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data |
| title_full | Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data |
| title_fullStr | Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data |
| title_full_unstemmed | Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data |
| title_short | Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data |
| title_sort | enhancing brain tumor detection a comparative study of cnn architectures using mri data |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03014.pdf |
| work_keys_str_mv | AT zhuzhimeng enhancingbraintumordetectionacomparativestudyofcnnarchitecturesusingmridata |