Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features
The human brain, primarily composed of white blood cells, is centered on the neurological system. Incorrectly positioned cells in the immune system, blood vessels, endocrine, glial, axon, and other cancer-causing tissues, can assemble to create a brain tumor. It is currently impossible to find cance...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/8/1451 |
| _version_ | 1797605825106149376 |
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| author | Mehwish Rasheed Muhammad Waseem Iqbal Arfan Jaffar Muhammad Usman Ashraf Khalid Ali Almarhabi Ahmed Mohammed Alghamdi Adel A. Bahaddad |
| author_facet | Mehwish Rasheed Muhammad Waseem Iqbal Arfan Jaffar Muhammad Usman Ashraf Khalid Ali Almarhabi Ahmed Mohammed Alghamdi Adel A. Bahaddad |
| author_sort | Mehwish Rasheed |
| collection | DOAJ |
| description | The human brain, primarily composed of white blood cells, is centered on the neurological system. Incorrectly positioned cells in the immune system, blood vessels, endocrine, glial, axon, and other cancer-causing tissues, can assemble to create a brain tumor. It is currently impossible to find cancer physically and make a diagnosis. The tumor can be found and recognized using the MRI-programmed division method. It takes a powerful segmentation technique to produce accurate output. This study examines a brain MRI scan and uses a technique to obtain a more precise image of the tumor-affected area. The critical aspects of the proposed method are the utilization of noisy MRI brain images, anisotropic noise removal filtering, segmentation with an SVM classifier, and isolation of the adjacent region from the normal morphological processes. Accurate brain MRI imaging is the primary goal of this strategy. The divided section of the cancer is placed on the actual image of a particular culture, but that is by no means the last step. The tumor is located by categorizing the pixel brightness in the filtered image. According to test findings, the SVM could partition data with 98% accuracy. |
| first_indexed | 2024-03-11T05:06:35Z |
| format | Article |
| id | doaj.art-eee4ee8bf7844013a2a78b1aec892e1a |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T05:06:35Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-eee4ee8bf7844013a2a78b1aec892e1a2023-11-17T18:55:14ZengMDPI AGDiagnostics2075-44182023-04-01138145110.3390/diagnostics13081451Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical FeaturesMehwish Rasheed0Muhammad Waseem Iqbal1Arfan Jaffar2Muhammad Usman Ashraf3Khalid Ali Almarhabi4Ahmed Mohammed Alghamdi5Adel A. Bahaddad6Department of Computer Science, Superior University, Lahore 54000, PakistanDepartment of Software Engineering, Superior University, Lahore 54000, PakistanDepartment of Computer Science, Superior University, Lahore 54000, PakistanDepartment of Computer Science, GC Women University, Sialkot 51310, PakistanDepartment of Computer Science, College of Computing in Al-Qunfudah, Umm Al-Qura University, Makkah 24381, Saudi ArabiaDepartment of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 21493, Saudi ArabiaDepartment of Information System, King Abdulaziz University, Jeddah 21589, Saudi ArabiaThe human brain, primarily composed of white blood cells, is centered on the neurological system. Incorrectly positioned cells in the immune system, blood vessels, endocrine, glial, axon, and other cancer-causing tissues, can assemble to create a brain tumor. It is currently impossible to find cancer physically and make a diagnosis. The tumor can be found and recognized using the MRI-programmed division method. It takes a powerful segmentation technique to produce accurate output. This study examines a brain MRI scan and uses a technique to obtain a more precise image of the tumor-affected area. The critical aspects of the proposed method are the utilization of noisy MRI brain images, anisotropic noise removal filtering, segmentation with an SVM classifier, and isolation of the adjacent region from the normal morphological processes. Accurate brain MRI imaging is the primary goal of this strategy. The divided section of the cancer is placed on the actual image of a particular culture, but that is by no means the last step. The tumor is located by categorizing the pixel brightness in the filtered image. According to test findings, the SVM could partition data with 98% accuracy.https://www.mdpi.com/2075-4418/13/8/1451brain tumormagnetic resonance imagingcontrast stretched enhancementanisotropicfiltrationsegmentation |
| spellingShingle | Mehwish Rasheed Muhammad Waseem Iqbal Arfan Jaffar Muhammad Usman Ashraf Khalid Ali Almarhabi Ahmed Mohammed Alghamdi Adel A. Bahaddad Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features Diagnostics brain tumor magnetic resonance imaging contrast stretched enhancement anisotropic filtration segmentation |
| title | Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features |
| title_full | Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features |
| title_fullStr | Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features |
| title_full_unstemmed | Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features |
| title_short | Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features |
| title_sort | recognizing brain tumors using adaptive noise filtering and statistical features |
| topic | brain tumor magnetic resonance imaging contrast stretched enhancement anisotropic filtration segmentation |
| url | https://www.mdpi.com/2075-4418/13/8/1451 |
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