Brain MRI image classification for tumor detection using integrated hybrid convolutional k-nearest neighbor model
In the field of medical image processing, brain tumor segmentation is one of the most important and challenging jobs since manual categorization by humans can lead to incorrect diagnosis and prognosis. Furthermore, it is a frustrating chore when there is a lot of data that has to be gathered. Becaus...
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Penerbit UTHM
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43900/1/Brain%20MRI%20image%20classification%20for%20tumor%20detection%20using%20integrated%20hybrid.pdf http://umpir.ump.edu.my/id/eprint/43900/ https://doi.org/10.30880/jscdm.2024.05.02.007 |
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| author | Hossain, Mirza Mahfuj Hasan, Md Mahmudul Islam, Ashraful Norizam, Sulaiman |
| author_facet | Hossain, Mirza Mahfuj Hasan, Md Mahmudul Islam, Ashraful Norizam, Sulaiman |
| author_sort | Hossain, Mirza Mahfuj |
| building | UMPSA Library |
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| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In the field of medical image processing, brain tumor segmentation is one of the most important and challenging jobs since manual categorization by humans can lead to incorrect diagnosis and prognosis. Furthermore, it is a frustrating chore when there is a lot of data that has to be gathered. Because brain tumors have a wide range of appearances and normal tissues and tumors are similar, it is difficult to separate specific tumor areas from pictures. Keeping this in mind, a preliminary processing method for brain MRI is presented in this study that applies Otsu's Thresholding and Morphological operation. An online image dataset (consisting of 3064 slices of brain images containing samples of meningioma, glioma, and pituitary tumor types) from 233 patients with a variety of tumor sizes, positions, forms, and intensity values of images is used for the experimental investigation. Lastly, we used Convolutional Neural Networks (CNN) and K-Nearest Neighbors (KNN) in the classical classification section. The hybrid Convolutional K-Nearest Neighbors (CKNN) model was then used, which produces superior results than the conventional used models. The primary goal of this study was to use brain MRI images to identify brain tumors. This study showed significant performance with accuracy of 89.88% for the hybrid CKNN model. |
| format | Article |
| id | my.ump.umpir.43900 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2024 |
| publisher | Penerbit UTHM |
| record_format | eprints |
| spelling | my.ump.umpir.439002025-02-25T08:21:17Z http://umpir.ump.edu.my/id/eprint/43900/ Brain MRI image classification for tumor detection using integrated hybrid convolutional k-nearest neighbor model Hossain, Mirza Mahfuj Hasan, Md Mahmudul Islam, Ashraful Norizam, Sulaiman T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering In the field of medical image processing, brain tumor segmentation is one of the most important and challenging jobs since manual categorization by humans can lead to incorrect diagnosis and prognosis. Furthermore, it is a frustrating chore when there is a lot of data that has to be gathered. Because brain tumors have a wide range of appearances and normal tissues and tumors are similar, it is difficult to separate specific tumor areas from pictures. Keeping this in mind, a preliminary processing method for brain MRI is presented in this study that applies Otsu's Thresholding and Morphological operation. An online image dataset (consisting of 3064 slices of brain images containing samples of meningioma, glioma, and pituitary tumor types) from 233 patients with a variety of tumor sizes, positions, forms, and intensity values of images is used for the experimental investigation. Lastly, we used Convolutional Neural Networks (CNN) and K-Nearest Neighbors (KNN) in the classical classification section. The hybrid Convolutional K-Nearest Neighbors (CKNN) model was then used, which produces superior results than the conventional used models. The primary goal of this study was to use brain MRI images to identify brain tumors. This study showed significant performance with accuracy of 89.88% for the hybrid CKNN model. Penerbit UTHM 2024 Article PeerReviewed pdf en cc_by_nc_sa_4 http://umpir.ump.edu.my/id/eprint/43900/1/Brain%20MRI%20image%20classification%20for%20tumor%20detection%20using%20integrated%20hybrid.pdf Hossain, Mirza Mahfuj and Hasan, Md Mahmudul and Islam, Ashraful and Norizam, Sulaiman (2024) Brain MRI image classification for tumor detection using integrated hybrid convolutional k-nearest neighbor model. Journal of Soft Computing and Data Mining, 5 (2). pp. 83-95. ISSN 2716-621X. (Published) https://doi.org/10.30880/jscdm.2024.05.02.007 https://doi.org/10.30880/jscdm.2024.05.02.007 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Hossain, Mirza Mahfuj Hasan, Md Mahmudul Islam, Ashraful Norizam, Sulaiman Brain MRI image classification for tumor detection using integrated hybrid convolutional k-nearest neighbor model |
| title | Brain MRI image classification for tumor detection using integrated hybrid convolutional k-nearest neighbor model |
| title_full | Brain MRI image classification for tumor detection using integrated hybrid convolutional k-nearest neighbor model |
| title_fullStr | Brain MRI image classification for tumor detection using integrated hybrid convolutional k-nearest neighbor model |
| title_full_unstemmed | Brain MRI image classification for tumor detection using integrated hybrid convolutional k-nearest neighbor model |
| title_short | Brain MRI image classification for tumor detection using integrated hybrid convolutional k-nearest neighbor model |
| title_sort | brain mri image classification for tumor detection using integrated hybrid convolutional k-nearest neighbor model |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/43900/1/Brain%20MRI%20image%20classification%20for%20tumor%20detection%20using%20integrated%20hybrid.pdf http://umpir.ump.edu.my/id/eprint/43900/ https://doi.org/10.30880/jscdm.2024.05.02.007 https://doi.org/10.30880/jscdm.2024.05.02.007 |
| url_provider | http://umpir.ump.edu.my/ |