Brain Tumor Diagnosis Using MR Image Processing Article Swipe
Sura Yarub Kamil
,
Mohammed Sahib Mahdi Altaei
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.22401/anjs.23.2.10
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.22401/anjs.23.2.10
Magnetic Resonance Imaging (MRI) images of brain are a process of high importance to diagnosing brain tumors.Brain tumor is an abnormal growth of cells in the brain.These tumors may be benign or malignant.The use of computer technologies became widely used to store and manage medical images for supporting medical decision and improve the accuracy of radiologists with a reduction of time in the interpretation of images.The present study aimed to establish a Computer-Aided Detection and Diagnosis (CADD) system dealing with medical MRI for classifying input digital image into normal or abnormal tumors, also the type of abnormal case is diagnosed into benign or malignant tumor.The proposed method is considered to be contained four stages within, they are: Pre-processing stage, image segmentation for determining the Region of Interest (ROI), Feature extraction based on Scale Invariant Feature Transform (SIFT) descriptor, and then classification.The results of classification are evaluated by cross validation technique, in which the dataset are divided into training set and testing set.To evaluate the achieved results, the classification is carried out using two levels for each case: logistics technique is used to check the results of normal case, and random forest to check the results of abnormal cases.Results of normal classification showed that the accuracy of applying Logistic Regression was 93.3%, whereas the classification score of abnormal cases was 99.9% for Random Forest, which ensure the success of the classification system and correct path of the computations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.22401/anjs.23.2.10
- https://anjs.edu.iq/index.php/anjs/article/download/2280/1787/
- OA Status
- diamond
- Cited By
- 3
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3041719075
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3041719075Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22401/anjs.23.2.10Digital Object Identifier
- Title
-
Brain Tumor Diagnosis Using MR Image ProcessingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-06-04Full publication date if available
- Authors
-
Sura Yarub Kamil, Mohammed Sahib Mahdi AltaeiList of authors in order
- Landing page
-
https://doi.org/10.22401/anjs.23.2.10Publisher landing page
- PDF URL
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https://anjs.edu.iq/index.php/anjs/article/download/2280/1787/Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://anjs.edu.iq/index.php/anjs/article/download/2280/1787/Direct OA link when available
- Concepts
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Scale-invariant feature transform, Random forest, Artificial intelligence, Computer science, Pattern recognition (psychology), Segmentation, Feature extraction, Image processing, Brain tumor, Magnetic resonance imaging, Computer-aided diagnosis, Radiology, Medicine, Image (mathematics), PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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10Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.system | 77, 230 |
| abstract_inverted_index.tumors | 27 |
| abstract_inverted_index.widely | 38 |
| abstract_inverted_index.Feature | 128, 134 |
| abstract_inverted_index.Forest, | 222 |
| abstract_inverted_index.Imaging | 2 |
| abstract_inverted_index.carried | 169 |
| abstract_inverted_index.correct | 232 |
| abstract_inverted_index.dataset | 153 |
| abstract_inverted_index.dealing | 78 |
| abstract_inverted_index.digital | 85 |
| abstract_inverted_index.divided | 155 |
| abstract_inverted_index.improve | 51 |
| abstract_inverted_index.medical | 44, 48, 80 |
| abstract_inverted_index.present | 66 |
| abstract_inverted_index.process | 9 |
| abstract_inverted_index.results | 141, 184, 194 |
| abstract_inverted_index.success | 226 |
| abstract_inverted_index.testing | 160 |
| abstract_inverted_index.tumors, | 91 |
| abstract_inverted_index.whereas | 211 |
| abstract_inverted_index.within, | 114 |
| abstract_inverted_index.Interest | 126 |
| abstract_inverted_index.Logistic | 207 |
| abstract_inverted_index.Magnetic | 0 |
| abstract_inverted_index.abnormal | 20, 90, 96, 196, 216 |
| abstract_inverted_index.accuracy | 53, 204 |
| abstract_inverted_index.achieved | 164 |
| abstract_inverted_index.applying | 206 |
| abstract_inverted_index.computer | 35 |
| abstract_inverted_index.decision | 49 |
| abstract_inverted_index.evaluate | 162 |
| abstract_inverted_index.proposed | 105 |
| abstract_inverted_index.results, | 165 |
| abstract_inverted_index.training | 157 |
| abstract_inverted_index.Detection | 73 |
| abstract_inverted_index.Diagnosis | 75 |
| abstract_inverted_index.Invariant | 133 |
| abstract_inverted_index.Resonance | 1 |
| abstract_inverted_index.Transform | 135 |
| abstract_inverted_index.contained | 111 |
| abstract_inverted_index.diagnosed | 99 |
| abstract_inverted_index.establish | 70 |
| abstract_inverted_index.evaluated | 145 |
| abstract_inverted_index.logistics | 177 |
| abstract_inverted_index.malignant | 103 |
| abstract_inverted_index.reduction | 58 |
| abstract_inverted_index.technique | 178 |
| abstract_inverted_index.tumor.The | 104 |
| abstract_inverted_index.Regression | 208 |
| abstract_inverted_index.considered | 108 |
| abstract_inverted_index.diagnosing | 14 |
| abstract_inverted_index.extraction | 129 |
| abstract_inverted_index.images.The | 65 |
| abstract_inverted_index.importance | 12 |
| abstract_inverted_index.supporting | 47 |
| abstract_inverted_index.technique, | 149 |
| abstract_inverted_index.validation | 148 |
| abstract_inverted_index.brain.These | 26 |
| abstract_inverted_index.classifying | 83 |
| abstract_inverted_index.descriptor, | 137 |
| abstract_inverted_index.determining | 122 |
| abstract_inverted_index.radiologists | 55 |
| abstract_inverted_index.segmentation | 120 |
| abstract_inverted_index.technologies | 36 |
| abstract_inverted_index.tumors.Brain | 16 |
| abstract_inverted_index.cases.Results | 197 |
| abstract_inverted_index.computations. | 236 |
| abstract_inverted_index.malignant.The | 32 |
| abstract_inverted_index.Computer-Aided | 72 |
| abstract_inverted_index.Pre-processing | 117 |
| abstract_inverted_index.classification | 143, 167, 200, 213, 229 |
| abstract_inverted_index.interpretation | 63 |
| abstract_inverted_index.classification.The | 140 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.54222067 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |