Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques
The detection, segmentation, and extraction from Magnetic Resonance Imaging (MRI) images of contaminated tumor areas are significant concerns; however, a repetitive and extensive task executed by radiologists or clinical experts relies on their expertise. Image processing concepts can imagine the va...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9166580/ |
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| author | Zheshu Jia Deyun Chen |
| author_facet | Zheshu Jia Deyun Chen |
| author_sort | Zheshu Jia |
| collection | DOAJ |
| description | The detection, segmentation, and extraction from Magnetic Resonance Imaging (MRI) images of contaminated tumor areas are significant concerns; however, a repetitive and extensive task executed by radiologists or clinical experts relies on their expertise. Image processing concepts can imagine the various anatomical structure of the human organ. Detection of human brain abnormal structures by basic imaging techniques is challenging. In this paper, a Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) has been proposed for brain tumor segmentation based on deep learning techniques. The present work proposes the separation of the whole cerebral venous system into MRI imaging with the addition of a new, fully automatic algorithm based on structural, morphological, and relaxometry details. The segmenting function is distinguished by a high level of uniformity between anatomy and the neighboring brain tissue. ELM is a type of learning algorithm consisting of one or more layers of hidden nodes. Such networks are used in various areas, including regression and classification. In brain MRI images, the probabilistic neural network classification system has been utilized for training and checking the accuracy of tumor detection in images. The numerical results show almost 98.51% accuracy in detecting abnormal and normal tissue from brain Magnetic Resonance images that demonstrate the efficiency of the system suggested. |
| format | Article |
| id | doaj-art-e2449fde883b444aab6b6164bc8d7aef |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e2449fde883b444aab6b6164bc8d7aef2025-08-20T02:45:41ZengIEEEIEEE Access2169-35362025-01-011312378312379210.1109/ACCESS.2020.30163199166580Brain Tumor Identification and Classification of MRI Images Using Deep Learning TechniquesZheshu Jia0https://orcid.org/0000-0001-6156-3374Deyun Chen1https://orcid.org/0000-0002-6228-3646School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin, ChinaThe detection, segmentation, and extraction from Magnetic Resonance Imaging (MRI) images of contaminated tumor areas are significant concerns; however, a repetitive and extensive task executed by radiologists or clinical experts relies on their expertise. Image processing concepts can imagine the various anatomical structure of the human organ. Detection of human brain abnormal structures by basic imaging techniques is challenging. In this paper, a Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) has been proposed for brain tumor segmentation based on deep learning techniques. The present work proposes the separation of the whole cerebral venous system into MRI imaging with the addition of a new, fully automatic algorithm based on structural, morphological, and relaxometry details. The segmenting function is distinguished by a high level of uniformity between anatomy and the neighboring brain tissue. ELM is a type of learning algorithm consisting of one or more layers of hidden nodes. Such networks are used in various areas, including regression and classification. In brain MRI images, the probabilistic neural network classification system has been utilized for training and checking the accuracy of tumor detection in images. The numerical results show almost 98.51% accuracy in detecting abnormal and normal tissue from brain Magnetic Resonance images that demonstrate the efficiency of the system suggested.https://ieeexplore.ieee.org/document/9166580/Brain tumor detectionclassificationsegmentationdeep learningELM |
| spellingShingle | Zheshu Jia Deyun Chen Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques IEEE Access Brain tumor detection classification segmentation deep learning ELM |
| title | Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques |
| title_full | Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques |
| title_fullStr | Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques |
| title_full_unstemmed | Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques |
| title_short | Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques |
| title_sort | brain tumor identification and classification of mri images using deep learning techniques |
| topic | Brain tumor detection classification segmentation deep learning ELM |
| url | https://ieeexplore.ieee.org/document/9166580/ |
| work_keys_str_mv | AT zheshujia braintumoridentificationandclassificationofmriimagesusingdeeplearningtechniques AT deyunchen braintumoridentificationandclassificationofmriimagesusingdeeplearningtechniques |