A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation
<italic>Goal:</italic> Implementation of an artificial intelli gence-based medical diagnosis tool for brain tumor classification, which is called the BTFSC-Net. <italic>Methods:</italic> Medical images are preprocessed using a hybrid probabilistic wiener filter (HPWF) The dee...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9928545/ |
| _version_ | 1797978879822921728 |
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| author | M. V. S. Ramprasad Md. Zia Ur Rahman Masreshaw Demelash Bayleyegn |
| author_facet | M. V. S. Ramprasad Md. Zia Ur Rahman Masreshaw Demelash Bayleyegn |
| author_sort | M. V. S. Ramprasad |
| collection | DOAJ |
| description | <italic>Goal:</italic> Implementation of an artificial intelli gence-based medical diagnosis tool for brain tumor classification, which is called the BTFSC-Net. <italic>Methods:</italic> Medical images are preprocessed using a hybrid probabilistic wiener filter (HPWF) The deep learning convolutional neural network (DLCNN) was utilized to fuse MRI and CT images with robust edge analysis (REA) properties, which are used to identify the slopes and edges of source images. Then, hybrid fuzzy c-means integrated k-means (HFCMIK) clustering is used to segment the disease affected region from the fused image. Further, hybrid features such as texture, colour, and low-level features are extracted from the fused image by using gray-level cooccurrence matrix (GLCM), redundant discrete wavelet transform (RDWT) descriptors. Finally, a deep learning based probabilistic neural network (DLPNN) is used to classify malignant and benign tumors. The BTFSC-Net attained 99.21% of segmentation accuracy and 99.46% of classification accuracy. <italic>Conclusions:</italic> The simulations showed that BTFSC-Net outperformed as compared to existing methods. |
| first_indexed | 2024-04-11T05:30:05Z |
| format | Article |
| id | doaj.art-28cc780cc8de4af59f7a0ba71f014fad |
| institution | Directory Open Access Journal |
| issn | 2644-1276 |
| language | English |
| last_indexed | 2024-04-11T05:30:05Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj.art-28cc780cc8de4af59f7a0ba71f014fad2022-12-23T00:00:50ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762022-01-01317818810.1109/OJEMB.2022.32171869928545A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK SegmentationM. V. S. Ramprasad0https://orcid.org/0000-0002-3640-1637Md. Zia Ur Rahman1https://orcid.org/0000-0002-4948-3870Masreshaw Demelash Bayleyegn2https://orcid.org/0000-0001-5613-7610Koneru Lakshmaiah Education Foundation, K L University, Guntur, IndiaDepartment of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, K L University, Vaddeswaram, Guntur, IndiaCenter of Biomedical Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Ethiopia<italic>Goal:</italic> Implementation of an artificial intelli gence-based medical diagnosis tool for brain tumor classification, which is called the BTFSC-Net. <italic>Methods:</italic> Medical images are preprocessed using a hybrid probabilistic wiener filter (HPWF) The deep learning convolutional neural network (DLCNN) was utilized to fuse MRI and CT images with robust edge analysis (REA) properties, which are used to identify the slopes and edges of source images. Then, hybrid fuzzy c-means integrated k-means (HFCMIK) clustering is used to segment the disease affected region from the fused image. Further, hybrid features such as texture, colour, and low-level features are extracted from the fused image by using gray-level cooccurrence matrix (GLCM), redundant discrete wavelet transform (RDWT) descriptors. Finally, a deep learning based probabilistic neural network (DLPNN) is used to classify malignant and benign tumors. The BTFSC-Net attained 99.21% of segmentation accuracy and 99.46% of classification accuracy. <italic>Conclusions:</italic> The simulations showed that BTFSC-Net outperformed as compared to existing methods.https://ieeexplore.ieee.org/document/9928545/Brain tumor segmentationclassificationfeature extractiondeep learning convolutional neural networkrobust edge analysis |
| spellingShingle | M. V. S. Ramprasad Md. Zia Ur Rahman Masreshaw Demelash Bayleyegn A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation IEEE Open Journal of Engineering in Medicine and Biology Brain tumor segmentation classification feature extraction deep learning convolutional neural network robust edge analysis |
| title | A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation |
| title_full | A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation |
| title_fullStr | A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation |
| title_full_unstemmed | A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation |
| title_short | A Deep Probabilistic Sensing and Learning Model for Brain Tumor Classification With Fusion-Net and HFCMIK Segmentation |
| title_sort | deep probabilistic sensing and learning model for brain tumor classification with fusion net and hfcmik segmentation |
| topic | Brain tumor segmentation classification feature extraction deep learning convolutional neural network robust edge analysis |
| url | https://ieeexplore.ieee.org/document/9928545/ |
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