Improvement of Automatic Glioma Brain Tumor Detection Using Deep Convolutional Neural Networks
- Title
- Improvement of Automatic Glioma Brain Tumor Detection Using Deep Convolutional Neural Networks
- Creator
- Altameem A.; Mallikarjuna B.; Saudagar A.K.J.; Sharma M.; Poonia R.C.
- Description
- This article introduces automatic brain tumor detection from a magnetic resonance image (MRI). It provides novel algorithms for extracting patches and segmentation trained with Convolutional Neural Network (CNN)'s to identify brain tumors. Further, this study provides deep learning and image segmentation with CNN algorithms. This contribution proposed two similar segmentation algorithms: one for the Higher Grade Gliomas (HGG) and the other for the Lower Grade Gliomas (LGG) for the brain tumor patients. The proposed algorithms (Intensity normalization, Patch extraction, Selecting the best patch, segmentation of HGG, and Segmentation of LGG) identify the gliomas and detect the stage of the tumor as per taking the MRI as input and segmented tumor from the MRIs and elaborated the four algorithms to detect HGG, and segmentation to detect the LGG works with CNN. The segmentation algorithm is compared with different existing algorithms and performs the automatic identification reasonably with high accuracy as per epochs generated with accuracy and loss curves. This article also described how transfer learning has helped extract the image and resolution of the image and increase the segmentation accuracy in the case of LGG patients. Copyright 2022, Mary Ann Liebert, Inc., publishers 2022.
- Source
- Journal of Computational Biology, Vol-29, No. 6, pp. 530-544.
- Date
- 2022-01-01
- Publisher
- Mary Ann Liebert Inc.
- Subject
- convolutional neurol networks; deep learning; higher grade gliomas; image segmentation; lower grade gliomas; voxel
- Coverage
- Altameem A., Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh, Saudi Arabia; Mallikarjuna B., School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Saudagar A.K.J., Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia; Sharma M., School of Computing Science and Engineering, Galgotias University, Greater Noida, India; Poonia R.C., Department of Computer Science, CHRIST (Deemed to Be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 10665277; PubMed ID: 35235381; CODEN: JCOBE
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Altameem A.; Mallikarjuna B.; Saudagar A.K.J.; Sharma M.; Poonia R.C., “Improvement of Automatic Glioma Brain Tumor Detection Using Deep Convolutional Neural Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/15024.