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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
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 : 11
Issue Published : 118
Article Submitted : 21686
Article Published : 8549
Total Authors : 22487
Total Reviewer : 811
Total Countries : 159
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| Paper Title: | Deep Learning in Neuro Oncology: an approach to Brain Tumor Detection |
| Authors Name: | Navya S K , Afroj Alam |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_203458
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| Published Paper Id: | IJRTI2505052 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | Brain is the central part of human nervous system, brain tumor are the abnormal growth of cells in the brain that leads to death. Brain tumors are the vital neurological disorder, the common and precise classification and segmentation of brain tumor is obtained through medical imagining techniques like MRI and CT scans of brain. However, detecting brain tumor using MRI scans images, usually depends on human interpretation, which adds more time, requires expertise and is prone to human errors. However, detecting brain tumors physically or manually is a very difficult task and time-consuming which might lead to imprecision. We have developed a brain tumor system that uses the CNN (Convolutional Neural Network) model and the U-Net design model to accurately identify and classify pituitary gland tumors, glioma tumors, and meningioma tumors in order to address this issue. To improve the performance of our suggested model, image enhancement techniques such as data augmentation techniques, which apply various filters to the original images, will be used to improve the visual representation of the MRI scans. A diverse range of cases, 3558 glioma photos, 2758 pituitary images, 3592 meningioma images, and 2580 non-tumor images make up the collected data. In this work, we presented a unique method that uses deep learning algorithms to detect tumors from MRI scan images. The suggested model outperformed the CNN and U-Net models in terms of brain tumor classification and segmentation, with testing accuracies of 97.54% and 99%, respectively. |
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| Cite Article: | "Deep Learning in Neuro Oncology: an approach to Brain Tumor Detection ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a497-a504, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505052.pdf |
| Downloads: | 000398 |
| 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: IJRTI2505052
Registration ID:203458
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: a497-a504 Country: bangalore, karnataka, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505052 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505052 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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