Segmentation and classification of brain tumor using 3D-UNet deep neural networks
Early detection and diagnosis of a brain tumor enhance the medical options and the patient's chance of recovery. Magnetic resonance imaging (MRI) is used to detect and diagnose brain tumors. However, the manual identification of brain tumors from a large number of MRI images in clinical practic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-06-01
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| Series: | International Journal of Cognitive Computing in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307422000213 |
| _version_ | 1797959195993047040 |
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| author | Pranjal Agrawal Nitish Katal Nishtha Hooda |
| author_facet | Pranjal Agrawal Nitish Katal Nishtha Hooda |
| author_sort | Pranjal Agrawal |
| collection | DOAJ |
| description | Early detection and diagnosis of a brain tumor enhance the medical options and the patient's chance of recovery. Magnetic resonance imaging (MRI) is used to detect and diagnose brain tumors. However, the manual identification of brain tumors from a large number of MRI images in clinical practice solely depends on the time and experience of medical professionals. Presently, computer aided expert systems are booming to facilitate medical diagnosis and treatment recommendations. Numerous machine learning and deep learning based frameworks are employed for brain tumor detection. This paper aims to design an efficient framework for brain tumor segmentation and classification using deep learning techniques. The study employs the 3D-UNet model for the volumetric segmentation of the MRI images, followed by the classification of the tumor using CNNs. The loss and precision diagrams are presented to establish the validity of the models. The performance of proposed models is measured, and the results are compared with those of other approaches reported in the literature. It is found that the proposed work is more efficacious than the state-of-the-art techniques. |
| first_indexed | 2024-04-11T00:30:04Z |
| format | Article |
| id | doaj.art-f951e823c635486fa155b33dbbd00eb3 |
| institution | Directory Open Access Journal |
| issn | 2666-3074 |
| language | English |
| last_indexed | 2024-04-11T00:30:04Z |
| publishDate | 2022-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj.art-f951e823c635486fa155b33dbbd00eb32023-01-08T04:15:03ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742022-06-013199210Segmentation and classification of brain tumor using 3D-UNet deep neural networksPranjal Agrawal0Nitish Katal1Nishtha Hooda2School of Electronics, Indian Institute of Information Technology Una, IndiaSchool of Electronics, Indian Institute of Information Technology Una, India; Corresponding author.School of Computing, Indian Institute of Information Technology Una, IndiaEarly detection and diagnosis of a brain tumor enhance the medical options and the patient's chance of recovery. Magnetic resonance imaging (MRI) is used to detect and diagnose brain tumors. However, the manual identification of brain tumors from a large number of MRI images in clinical practice solely depends on the time and experience of medical professionals. Presently, computer aided expert systems are booming to facilitate medical diagnosis and treatment recommendations. Numerous machine learning and deep learning based frameworks are employed for brain tumor detection. This paper aims to design an efficient framework for brain tumor segmentation and classification using deep learning techniques. The study employs the 3D-UNet model for the volumetric segmentation of the MRI images, followed by the classification of the tumor using CNNs. The loss and precision diagrams are presented to establish the validity of the models. The performance of proposed models is measured, and the results are compared with those of other approaches reported in the literature. It is found that the proposed work is more efficacious than the state-of-the-art techniques.http://www.sciencedirect.com/science/article/pii/S2666307422000213Brain tumor segmentation, Brain tumor classification3D U-NetDeep learningConvolutional neural networkMRINeural networks |
| spellingShingle | Pranjal Agrawal Nitish Katal Nishtha Hooda Segmentation and classification of brain tumor using 3D-UNet deep neural networks International Journal of Cognitive Computing in Engineering Brain tumor segmentation, Brain tumor classification 3D U-Net Deep learning Convolutional neural network MRI Neural networks |
| title | Segmentation and classification of brain tumor using 3D-UNet deep neural networks |
| title_full | Segmentation and classification of brain tumor using 3D-UNet deep neural networks |
| title_fullStr | Segmentation and classification of brain tumor using 3D-UNet deep neural networks |
| title_full_unstemmed | Segmentation and classification of brain tumor using 3D-UNet deep neural networks |
| title_short | Segmentation and classification of brain tumor using 3D-UNet deep neural networks |
| title_sort | segmentation and classification of brain tumor using 3d unet deep neural networks |
| topic | Brain tumor segmentation, Brain tumor classification 3D U-Net Deep learning Convolutional neural network MRI Neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2666307422000213 |
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