Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data
In modern healthcare, the precision of medical image segmentation holds immense significance for diagnosis and treatment planning. Deep learning techniques, such as CNNs, UNETs, and Transformers, have revolutionized this field by automating the previously labor-intensive manual segmentation processe...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823003476 |
| _version_ | 1797629737167749120 |
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| author | Abdul Haseeb Nizamani Zhigang Chen Ahsan Ahmed Nizamani Uzair Aslam Bhatti |
| author_facet | Abdul Haseeb Nizamani Zhigang Chen Ahsan Ahmed Nizamani Uzair Aslam Bhatti |
| author_sort | Abdul Haseeb Nizamani |
| collection | DOAJ |
| description | In modern healthcare, the precision of medical image segmentation holds immense significance for diagnosis and treatment planning. Deep learning techniques, such as CNNs, UNETs, and Transformers, have revolutionized this field by automating the previously labor-intensive manual segmentation processes. However, challenges like intricate structures and indistinct features persist, leading to accuracy issues. Researchers are diligently addressing these challenges to further unlock the potential of medical image segmentation in healthcare transformation. To enhance the precision of brain tumor MRI image segmentation, our study introduces three novel feature-enhanced hybrid UNet models (FE-HU-NET): FE1-HU-NET, FE2-HU-NET, and FE3-HU-NET. Our approach encompasses three main aspects. Initially, we emphasize feature enhancement during the image preprocessing stage. We apply distinct image enhancement techniques—CLAHE, MHE, and MBOBHE—to each model. Secondly, we tailor the architecture of the UNet model to enhance segmentation results, focusing on a personalized layered design. Lastly, we employ a CNN model in post-processing to refine segmentation outcomes through additional convolutional layers. The HU-Net module, shared across the three models, integrates a customized UNet layer and a CNN. We also introduce an alternative feature-enhanced variant, FE4-HU-NET, utilizing the DeepLABv3 model. Incorporating CLAHE for image enhancement and bolstered by CNN layers, this variant offers a distinct approach. Rigorous experimentation underscores the excellence of our proposed framework in distinguishing complex brain tissues, surpassing current state-of-the-art models. Impressively, we achieve accuracy rates exceeding 99% across two publicly available datasets. Performance metrics such as the Jaccard index, sensitivity, and specificity further substantiate the effectiveness of our Hybrid U-Net model. |
| first_indexed | 2024-03-11T10:57:14Z |
| format | Article |
| id | doaj.art-62c10adbb8a941aaafc69bc8f286f35f |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-11T10:57:14Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-62c10adbb8a941aaafc69bc8f286f35f2023-11-13T04:09:06ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-10-01359101793Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI dataAbdul Haseeb Nizamani0Zhigang Chen1Ahsan Ahmed Nizamani2Uzair Aslam Bhatti3School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, China; Corresponding authors.School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Information and Communication Engineering, Hainan University, Haikou 570100, China; Corresponding authors.In modern healthcare, the precision of medical image segmentation holds immense significance for diagnosis and treatment planning. Deep learning techniques, such as CNNs, UNETs, and Transformers, have revolutionized this field by automating the previously labor-intensive manual segmentation processes. However, challenges like intricate structures and indistinct features persist, leading to accuracy issues. Researchers are diligently addressing these challenges to further unlock the potential of medical image segmentation in healthcare transformation. To enhance the precision of brain tumor MRI image segmentation, our study introduces three novel feature-enhanced hybrid UNet models (FE-HU-NET): FE1-HU-NET, FE2-HU-NET, and FE3-HU-NET. Our approach encompasses three main aspects. Initially, we emphasize feature enhancement during the image preprocessing stage. We apply distinct image enhancement techniques—CLAHE, MHE, and MBOBHE—to each model. Secondly, we tailor the architecture of the UNet model to enhance segmentation results, focusing on a personalized layered design. Lastly, we employ a CNN model in post-processing to refine segmentation outcomes through additional convolutional layers. The HU-Net module, shared across the three models, integrates a customized UNet layer and a CNN. We also introduce an alternative feature-enhanced variant, FE4-HU-NET, utilizing the DeepLABv3 model. Incorporating CLAHE for image enhancement and bolstered by CNN layers, this variant offers a distinct approach. Rigorous experimentation underscores the excellence of our proposed framework in distinguishing complex brain tissues, surpassing current state-of-the-art models. Impressively, we achieve accuracy rates exceeding 99% across two publicly available datasets. Performance metrics such as the Jaccard index, sensitivity, and specificity further substantiate the effectiveness of our Hybrid U-Net model.http://www.sciencedirect.com/science/article/pii/S1319157823003476Medical Image SegmentationUNetCLAHEFeature enhancementMRI |
| spellingShingle | Abdul Haseeb Nizamani Zhigang Chen Ahsan Ahmed Nizamani Uzair Aslam Bhatti Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data Journal of King Saud University: Computer and Information Sciences Medical Image Segmentation UNet CLAHE Feature enhancement MRI |
| title | Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data |
| title_full | Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data |
| title_fullStr | Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data |
| title_full_unstemmed | Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data |
| title_short | Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data |
| title_sort | advance brain tumor segmentation using feature fusion methods with deep u net model with cnn for mri data |
| topic | Medical Image Segmentation UNet CLAHE Feature enhancement MRI |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823003476 |
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