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Title |
Performance analysis the of Cerebrospinal Fluid (CSF) Cell Diseases |
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Author |
Soobia Saeed, Afnizanfaizal Bin Abdullah, NZ Jhanjhi, Muneer Ahmad |
| Citation |
Vol. 25 No. 12 pp. 152-164 |
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Abstract |
Medicinal images assume a key part in the diagnosis of tumors as well as Cerebrospinal fluid (CSF) leak. In a similar way, MRI could be the segmentation control regenerative imaging technology, which permits angle sectional perspective of the body which gives convenience to specialists, will inspect the affected person. The image sent by MRI is detailed which enables it to recognize minor improvements of structures throughout the body and is usually a basic part in finding and treatment planning. In this research, the author had endeavored the approach is to classify MRI images (4-Dimensional) either at the beginning of production to have a tumor can be utilized for tumor recognition. The point of the examination is to address the previously mentioned issues related with exactness and increment the productivity of cerebrum disease picture or CSF leakage (discover fluid or liquid within brain). Our aim of this research is to construct a framework that can identify cancer damage area or be isolated from tumors and non-tumors quiet by using 4D image light field segmentation which is followed by MATLAB modelling techniques and to measure the size of brain damage cells deep inside of CSF. Data is usually collected from the SVM Tool by using MATLAB included K-Nearest Neighbor (KNN) Algorithm. The researcher proposes a 4Dimentional modulation method that oversees the light field that can be used for the light editing field. Depending on the input of the user, objective evaluation of each ray is evaluated using the KNN to maintain the 4D frequency (redundancy) light fields. These Methods of light fields can be helpful for improving the quality of application editing segmentation and light field composite pipeline, as they reduce boundary artefacts. the specialist centers on cerebrum tumor and applies a testing for the images of the brain tumor. It at that point goes on depict the arrangement in the therapeutic terms and usage and furthermore gives some forecast about the future created by modified innovation. |
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Keywords |
Brain Tumor, MRI, Image segmentation, CSF, KNN |
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