Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images
Abstract Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast and clarity, than any alternative scanning process. Manually separating BTs from...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84692-7 |
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| author | K. Lakshmi Sibi Amaran G. Subbulakshmi S. Padmini Gyanenedra Prasad Joshi Woong Cho |
| author_facet | K. Lakshmi Sibi Amaran G. Subbulakshmi S. Padmini Gyanenedra Prasad Joshi Woong Cho |
| author_sort | K. Lakshmi |
| collection | DOAJ |
| description | Abstract Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast and clarity, than any alternative scanning process. Manually separating BTs from several MRI images gathered in medical practice for cancer analysis is challenging and time-consuming. Tumours and MRI scans of the brain are exposed utilizing methods and machine learning technologies, simplifying the process for doctors. MRI images can sometimes appear normal even when a patient has a tumour or malignancy. Deep learning approaches have recently depended on deep convolutional neural networks to analyze medical images with promising outcomes. It supports saving lives faster and rectifying some medical errors. With this motivation, this article presents a new explainable artificial intelligence with semantic segmentation and Bayesian machine learning for brain tumors (XAISS-BMLBT) technique. The presented XAISS-BMLBT technique mainly concentrates on the semantic segmentation and classification of BT in MRI images. The presented XAISS-BMLBT approach initially involves bilateral filtering-based image pre-processing to eliminate the noise. Next, the XAISS-BMLBT technique performs the MEDU-Net+ segmentation process to define the impacted brain regions. For the feature extraction process, the ResNet50 model is utilized. Furthermore, the Bayesian regularized artificial neural network (BRANN) model is used to identify the presence of BTs. Finally, an improved radial movement optimization model is employed for the hyperparameter tuning of the BRANN technique. To highlight the improved performance of the XAISS-BMLBT technique, a series of simulations were accomplished by utilizing a benchmark database. The experimental validation of the XAISS-BMLBT technique portrayed a superior accuracy value of 97.75% over existing models. |
| format | Article |
| id | doaj-art-a4e666db43d148b88dc03de8760ef420 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a4e666db43d148b88dc03de8760ef4202025-08-20T02:46:13ZengNature PortfolioScientific Reports2045-23222025-01-0115112210.1038/s41598-024-84692-7Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI imagesK. Lakshmi0Sibi Amaran1G. Subbulakshmi2S. Padmini3Gyanenedra Prasad Joshi4Woong Cho5Department of Information Technology, Sri Manakula Vinayagar Engineering CollegeDepartment of Computing Technologies, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and TechnologyDepartment of Artificial Intelligence and Data Science, St. Joseph’s College of Engineering and TechnologyDepartment of Computing Technologies, SRM Institute of Science and TechnologyDepartment of AI Software, Kangwon National UniversityDepartment of Electronics, Information and Communication Engineering, Kangwon National UniversityAbstract Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast and clarity, than any alternative scanning process. Manually separating BTs from several MRI images gathered in medical practice for cancer analysis is challenging and time-consuming. Tumours and MRI scans of the brain are exposed utilizing methods and machine learning technologies, simplifying the process for doctors. MRI images can sometimes appear normal even when a patient has a tumour or malignancy. Deep learning approaches have recently depended on deep convolutional neural networks to analyze medical images with promising outcomes. It supports saving lives faster and rectifying some medical errors. With this motivation, this article presents a new explainable artificial intelligence with semantic segmentation and Bayesian machine learning for brain tumors (XAISS-BMLBT) technique. The presented XAISS-BMLBT technique mainly concentrates on the semantic segmentation and classification of BT in MRI images. The presented XAISS-BMLBT approach initially involves bilateral filtering-based image pre-processing to eliminate the noise. Next, the XAISS-BMLBT technique performs the MEDU-Net+ segmentation process to define the impacted brain regions. For the feature extraction process, the ResNet50 model is utilized. Furthermore, the Bayesian regularized artificial neural network (BRANN) model is used to identify the presence of BTs. Finally, an improved radial movement optimization model is employed for the hyperparameter tuning of the BRANN technique. To highlight the improved performance of the XAISS-BMLBT technique, a series of simulations were accomplished by utilizing a benchmark database. The experimental validation of the XAISS-BMLBT technique portrayed a superior accuracy value of 97.75% over existing models.https://doi.org/10.1038/s41598-024-84692-7Brain tumorsHyperparameter tuningExplainable artificial intelligenceSemantic segmentationBayesian machine learning |
| spellingShingle | K. Lakshmi Sibi Amaran G. Subbulakshmi S. Padmini Gyanenedra Prasad Joshi Woong Cho Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images Scientific Reports Brain tumors Hyperparameter tuning Explainable artificial intelligence Semantic segmentation Bayesian machine learning |
| title | Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images |
| title_full | Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images |
| title_fullStr | Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images |
| title_full_unstemmed | Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images |
| title_short | Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images |
| title_sort | explainable artificial intelligence with unet based segmentation and bayesian machine learning for classification of brain tumors using mri images |
| topic | Brain tumors Hyperparameter tuning Explainable artificial intelligence Semantic segmentation Bayesian machine learning |
| url | https://doi.org/10.1038/s41598-024-84692-7 |
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