YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image
Brain tumor detection and segmentation are the main issues in biomedical engineering research fields, and it is always challenging due to its heterogeneous shape and location in MRI. The quality of the MR images also plays an important role in providing a clear sight of the shape and boundary of the...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/3819801 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558623643926528 |
|---|---|
| author | Nur Iriawan Anindya A. Pravitasari Ulfa S. Nuraini Nur I. Nirmalasari Taufik Azmi Muhammad Nasrudin Adam F. Fandisyah Kartika Fithriasari Santi W. Purnami null Irhamah Widiana Ferriastuti |
| author_facet | Nur Iriawan Anindya A. Pravitasari Ulfa S. Nuraini Nur I. Nirmalasari Taufik Azmi Muhammad Nasrudin Adam F. Fandisyah Kartika Fithriasari Santi W. Purnami null Irhamah Widiana Ferriastuti |
| author_sort | Nur Iriawan |
| collection | DOAJ |
| description | Brain tumor detection and segmentation are the main issues in biomedical engineering research fields, and it is always challenging due to its heterogeneous shape and location in MRI. The quality of the MR images also plays an important role in providing a clear sight of the shape and boundary of the tumor. The clear shape and boundary of the tumor will increase the probability of safe medical surgery. Analysis of this different scope of image types requires refined computerized quantification and visualization tools. This paper employed deep learning to detect and segment brain tumor MRI images by combining the convolutional neural network (CNN) and fully convolutional network (FCN) methodology in serial. The fundamental finding is to detect and localize the tumor area with YOLO-CNN and segment it with the FCN-UNet architecture. This analysis provided automatic detection and segmentation as well as the location of the tumor. The segmentation using the UNet is run under four scenarios, and the best one is chosen by the minimum loss and maximum accuracy value. In this research, we used 277 images for training, 69 images for validation, and 14 images for testing. The validation is carried out by comparing the segmentation results with the medical ground truth to provide the correct classification ratio (CCR). This study succeeded in the detection of brain tumors and provided a clear area of the brain tumor with a high CCR of about 97%. |
| format | Article |
| id | doaj-art-deef4881ca63433eb72bc513e50dec37 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-deef4881ca63433eb72bc513e50dec372025-02-03T01:31:59ZengWileyApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/3819801YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor ImageNur Iriawan0Anindya A. Pravitasari1Ulfa S. Nuraini2Nur I. Nirmalasari3Taufik Azmi4Muhammad Nasrudin5Adam F. Fandisyah6Kartika Fithriasari7Santi W. Purnami8null Irhamah9Widiana Ferriastuti10Department of StatisticsDepartment of StatisticsDepartment of StatisticsDepartment of StatisticsDepartment of StatisticsDepartment of StatisticsDepartment of StatisticsDepartment of StatisticsDepartment of StatisticsDepartment of StatisticsDepartment of RadiologyBrain tumor detection and segmentation are the main issues in biomedical engineering research fields, and it is always challenging due to its heterogeneous shape and location in MRI. The quality of the MR images also plays an important role in providing a clear sight of the shape and boundary of the tumor. The clear shape and boundary of the tumor will increase the probability of safe medical surgery. Analysis of this different scope of image types requires refined computerized quantification and visualization tools. This paper employed deep learning to detect and segment brain tumor MRI images by combining the convolutional neural network (CNN) and fully convolutional network (FCN) methodology in serial. The fundamental finding is to detect and localize the tumor area with YOLO-CNN and segment it with the FCN-UNet architecture. This analysis provided automatic detection and segmentation as well as the location of the tumor. The segmentation using the UNet is run under four scenarios, and the best one is chosen by the minimum loss and maximum accuracy value. In this research, we used 277 images for training, 69 images for validation, and 14 images for testing. The validation is carried out by comparing the segmentation results with the medical ground truth to provide the correct classification ratio (CCR). This study succeeded in the detection of brain tumors and provided a clear area of the brain tumor with a high CCR of about 97%.http://dx.doi.org/10.1155/2024/3819801 |
| spellingShingle | Nur Iriawan Anindya A. Pravitasari Ulfa S. Nuraini Nur I. Nirmalasari Taufik Azmi Muhammad Nasrudin Adam F. Fandisyah Kartika Fithriasari Santi W. Purnami null Irhamah Widiana Ferriastuti YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image Applied Computational Intelligence and Soft Computing |
| title | YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image |
| title_full | YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image |
| title_fullStr | YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image |
| title_full_unstemmed | YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image |
| title_short | YOLO-UNet Architecture for Detecting and Segmenting the Localized MRI Brain Tumor Image |
| title_sort | yolo unet architecture for detecting and segmenting the localized mri brain tumor image |
| url | http://dx.doi.org/10.1155/2024/3819801 |
| work_keys_str_mv | AT nuririawan yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage AT anindyaapravitasari yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage AT ulfasnuraini yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage AT nurinirmalasari yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage AT taufikazmi yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage AT muhammadnasrudin yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage AT adamffandisyah yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage AT kartikafithriasari yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage AT santiwpurnami yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage AT nullirhamah yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage AT widianaferriastuti yolounetarchitecturefordetectingandsegmentingthelocalizedmribraintumorimage |