Design and development of a deep learning model for brain abnormality detection using MRI
The research aims to develop a DL model for the detection of abnormalities in MRI images that works as an automated and accurate detection system that assists health care professionals in diagnosing the abnormalities in brain. In this research, an advanced brain abnormality prediction model associat...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21681163.2023.2250878 |
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| author | Mahesh P Potadar Raghunath S Holambe Rajan H Chile |
| author_facet | Mahesh P Potadar Raghunath S Holambe Rajan H Chile |
| author_sort | Mahesh P Potadar |
| collection | DOAJ |
| description | The research aims to develop a DL model for the detection of abnormalities in MRI images that works as an automated and accurate detection system that assists health care professionals in diagnosing the abnormalities in brain. In this research, an advanced brain abnormality prediction model associated with the deep Convolutional Neural Network (CNN) is implemented. The main advantage of this research is the proposed sonar emigration optimization that uses sonaring behaviour for predicting the position of the target with an improved convergence rate. Additionally, intensity, texture and shape-based features extract significant features for enhancing the prediction results. The sonar emigration-based deep CNN-based classifier attained the values of 95.46%, 95.72%, 94.56%, and 96.39% for dataset-1 during TP 90 for accuracy, sensitivity, specificity, and F1 score. For dataset-2 the proposed method attained the values of 94.15%,94.40%,93.25% and 95.07%, during the TP 90 while measuring the metrics, which is quite more efficient than other methods. |
| format | Article |
| id | doaj-art-e63035896c7b44c7a39360fad4e5579b |
| institution | Kabale University |
| issn | 2168-1163 2168-1171 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization |
| spelling | doaj-art-e63035896c7b44c7a39360fad4e5579b2024-11-29T10:29:56ZengTaylor & Francis GroupComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2168-11632168-11712024-12-0112110.1080/21681163.2023.2250878Design and development of a deep learning model for brain abnormality detection using MRIMahesh P Potadar0Raghunath S Holambe1Rajan H Chile2Electronics & Telecommunication Engineering, PVG’s College of Engineering and Technology & GKPIOM, Pune, IndiaDepartment of Instrumentation Engineering, SGGS Institute of Engineering & Technology, Swami Ramanand Teerth University, Nanded, IndiaDepartment of Instrumentation Engineering, SGGS Institute of Engineering & Technology, Swami Ramanand Teerth University, Nanded, IndiaThe research aims to develop a DL model for the detection of abnormalities in MRI images that works as an automated and accurate detection system that assists health care professionals in diagnosing the abnormalities in brain. In this research, an advanced brain abnormality prediction model associated with the deep Convolutional Neural Network (CNN) is implemented. The main advantage of this research is the proposed sonar emigration optimization that uses sonaring behaviour for predicting the position of the target with an improved convergence rate. Additionally, intensity, texture and shape-based features extract significant features for enhancing the prediction results. The sonar emigration-based deep CNN-based classifier attained the values of 95.46%, 95.72%, 94.56%, and 96.39% for dataset-1 during TP 90 for accuracy, sensitivity, specificity, and F1 score. For dataset-2 the proposed method attained the values of 94.15%,94.40%,93.25% and 95.07%, during the TP 90 while measuring the metrics, which is quite more efficient than other methods.https://www.tandfonline.com/doi/10.1080/21681163.2023.2250878Brain abnormalityMRI imagebrain tumourdeep convolutional neural networksonar emigration optimisationTP |
| spellingShingle | Mahesh P Potadar Raghunath S Holambe Rajan H Chile Design and development of a deep learning model for brain abnormality detection using MRI Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization Brain abnormality MRI image brain tumour deep convolutional neural network sonar emigration optimisation TP |
| title | Design and development of a deep learning model for brain abnormality detection using MRI |
| title_full | Design and development of a deep learning model for brain abnormality detection using MRI |
| title_fullStr | Design and development of a deep learning model for brain abnormality detection using MRI |
| title_full_unstemmed | Design and development of a deep learning model for brain abnormality detection using MRI |
| title_short | Design and development of a deep learning model for brain abnormality detection using MRI |
| title_sort | design and development of a deep learning model for brain abnormality detection using mri |
| topic | Brain abnormality MRI image brain tumour deep convolutional neural network sonar emigration optimisation TP |
| url | https://www.tandfonline.com/doi/10.1080/21681163.2023.2250878 |
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