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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19482
Article Published : 8050
Total Authors : 21282
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Published Paper Details
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| Paper Title: | Brain Tumor Detection with Transfer Learning |
| Authors Name: | Mohammad Maaz , Rahul Yadav |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_187482
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| Published Paper Id: | IJRTI2307008 |
| Published In: | Volume 8 Issue 7, July-2023 |
| DOI: | |
| Abstract: | Brain tumors are one of the leading causes of death and disability worldwide. Early detection and treatment of brain tumors is important to improve patient outcomes. However, brain tumors are often difficult to diagnose and diagnose, especially in the early stages. Transfer learning is a machine learning technique that can be used to improve the performance of new tasks using patterns learned in related tasks. In the context of brain tumor diagnosis, transfer learning can be used to improve the performance of the model by using what it learns from a large database of MRI scans of the brain. In this study, we use transformative learning to develop the model. For the diagnosis of brain tumors on magnetic resonance imaging. We use the convolutional neural network (CNN) VGG-16 as the base model. CNN VGG-16 previously studied large datasets of natural images. We fitted the CNN VGG-16 to the brain MRI dataset. The database contains 1,000 brain MRI images, 500 of which are labeled with a brain tumor and 500 of which are labeled without a brain tumor. We evaluated the performance of our Model using a test suite of 200 brain MRI images. The model achieved 98% accuracy in testing. This significantly improves the accuracy of training models that do not include learning transitions. The results of this study show that adaptive learning can be used to improve the performance of neuroimaging models in magnetic resonance imaging. The model developed in this study achieved 98 percent accuracy in multiple brain MRI scans. This significantly improves the accuracy of training models that do not include learning transitions. |
| Keywords: | Brain Tumor, MRI images,neural network |
| Cite Article: | "Brain Tumor Detection with Transfer Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 7, page no.47 - 49, July-2023, Available :http://www.ijrti.org/papers/IJRTI2307008.pdf |
| Downloads: | 000205142 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2307008
Registration ID:187482
Published In: Volume 8 Issue 7, July-2023
DOI (Digital Object Identifier):
Page No: 47 - 49 Country: Lucknow, Uttar Pradesh, India Research Area: Master of Computer Application Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2307008 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2307008 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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