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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | A Comprehensive Review of Automatic Brain Tumor Detection using Convolutional Neural Networks (CNNs) |
| Authors Name: | Prof. Usha L. Barad |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_207810
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| Published Paper Id: | IJNRD2310386 |
| Published In: | Volume 8 Issue 10, October-2023 |
| DOI: | |
| Abstract: | Medical science has incredibly grown and become Successful in modern years. Technology is altering the world of medicine. The main objective of our project is to detect the brain tumour by using Convolutional Neural Network (CNN). A Convolutional Neural Network is a classification of deep neural networks. CNN is mainly used for Image Processing, by which we will capture the image and compress it. The tumor detection is major challenging task in brain tumor quantitative evaluation. In recent years, owing to non-invasive and strong soft tissue comparison, Magnetic Resonance Imaging (MRI) has gained great interest. MRI is a commonly used image modality technique to locate brain tumors. An immense amount of data is produced by the MRI. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions. In recent years, deep learning methods have shown promising performance in solving various computer vision problems, such as image classification, object detection and semantic segmentation. A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results. Considering the remarkable breakthroughs made by state-of-the-art technologies, we provide this survey with a comprehensive study of recently developed deep learning based brain tumor segmentation techniques. More than 120 scientific papers are selected and discussed in this survey, extensively covering technical aspects such as network architecture design, segmentation under imbalanced conditions, and multi-modality processes. We also provide insightful discussions for future development directions. |
| Keywords: | Deep Neural Network, Brain Tumor, Magnetic Resonance Imaging (MRI), Datasets, Filter, CNN |
| Cite Article: | "A Comprehensive Review of Automatic Brain Tumor Detection using Convolutional Neural Networks (CNNs)", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 10, page no.d633-d642, October-2023, Available :http://www.ijnrd.org/papers/IJNRD2310386.pdf |
| Downloads: | 000118757 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2310386 Registration ID: 207810 Published In: Volume 8 Issue 10, October-2023 DOI (Digital Object Identifier): Page No: d633-d642 Country: PALANPUR, GUJARAT, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2310386 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2310386 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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