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In the growing age of various diseases and specially the Brain Tumor, Automatic differentiation of clinical pictures is not less than any boon. This technology helps in diagnosis, growth observation and also helps to tackle up through the disease. The brain tumor is one of the foremost rigorous diseases in medical science. An efficient and efficient analysis is often a key concern for the radiotherapist within the premature section of tumor growth. Histological grading, supported by a stereotactic diagnostic assay test, is the gold customary and therefore the convention for detective work the grade of a brain tumor. The biopsy procedure needs the operating surgeon to drill a little hole into the so that the tissue is collected. There are several risk factors involving the diagnostic test, as well as haemorrhage from the growth and brain inflicting infection, seizures, severe migraine, stroke, coma, and even death. However, the most concern with the stereotactic biopsy is that it's not 100% accurate which can lead to a heavy diagnostic error followed by wrong clinical management of the disease.
| Autorius: | Harsh Jindal, Atul Jha, Vaibhav Singh, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2022 |
| Knygos puslapių skaičius: | 104 |
| ISBN-10: | 6204982877 |
| ISBN-13: | 9786204982878 |
| Formatas: | 220 x 150 x 7 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Classification Of Brain Tumor Using Deep Learning: Depth analysis of brain tumors along with deep learning“
