Performance comparison of deep learning models for MRI-based brain tumor detection
Brain tumors pose a significant threat to human health, as they can severely affect both physical well-being and quality of life. These tumors often lead to increased intracranial pressure and neurological complications. Traditionally, brain tumors are diagnosed through manual interpretation via med...
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-01-01
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| Series: | AIMS Bioengineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/bioeng.2025001 |
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| author | Abdulmajeed Alsufyani |
| author_facet | Abdulmajeed Alsufyani |
| author_sort | Abdulmajeed Alsufyani |
| collection | DOAJ |
| description | Brain tumors pose a significant threat to human health, as they can severely affect both physical well-being and quality of life. These tumors often lead to increased intracranial pressure and neurological complications. Traditionally, brain tumors are diagnosed through manual interpretation via medical imaging techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). While these methods are effective, they are time-consuming and subject to errors in consistency and accuracy. This study explored the use of several deep-learning models, including YOLOv8, YOLOv9, Faster R-CNN, and ResNet18, for the detection of brain tumors from MR images. The results demonstrate that YOLOv9 outperforms the other models in terms of accuracy, precision, and recall, highlighting its potential as the most effective deep-learning approach for brain tumor detection. |
| format | Article |
| id | doaj-art-d747bf33796047c29d93435f37b7ec4d |
| institution | DOAJ |
| issn | 2375-1495 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Bioengineering |
| spelling | doaj-art-d747bf33796047c29d93435f37b7ec4d2025-08-20T03:08:57ZengAIMS PressAIMS Bioengineering2375-14952025-01-0112112110.3934/bioeng.2025001Performance comparison of deep learning models for MRI-based brain tumor detectionAbdulmajeed Alsufyani0Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaBrain tumors pose a significant threat to human health, as they can severely affect both physical well-being and quality of life. These tumors often lead to increased intracranial pressure and neurological complications. Traditionally, brain tumors are diagnosed through manual interpretation via medical imaging techniques such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). While these methods are effective, they are time-consuming and subject to errors in consistency and accuracy. This study explored the use of several deep-learning models, including YOLOv8, YOLOv9, Faster R-CNN, and ResNet18, for the detection of brain tumors from MR images. The results demonstrate that YOLOv9 outperforms the other models in terms of accuracy, precision, and recall, highlighting its potential as the most effective deep-learning approach for brain tumor detection.https://www.aimspress.com/article/doi/10.3934/bioeng.2025001deep learningbrain tumor detectionyolov9 |
| spellingShingle | Abdulmajeed Alsufyani Performance comparison of deep learning models for MRI-based brain tumor detection AIMS Bioengineering deep learning brain tumor detection yolov9 |
| title | Performance comparison of deep learning models for MRI-based brain tumor detection |
| title_full | Performance comparison of deep learning models for MRI-based brain tumor detection |
| title_fullStr | Performance comparison of deep learning models for MRI-based brain tumor detection |
| title_full_unstemmed | Performance comparison of deep learning models for MRI-based brain tumor detection |
| title_short | Performance comparison of deep learning models for MRI-based brain tumor detection |
| title_sort | performance comparison of deep learning models for mri based brain tumor detection |
| topic | deep learning brain tumor detection yolov9 |
| url | https://www.aimspress.com/article/doi/10.3934/bioeng.2025001 |
| work_keys_str_mv | AT abdulmajeedalsufyani performancecomparisonofdeeplearningmodelsformribasedbraintumordetection |