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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: | Brain Tumor Detection Using Convolution Neural Network |
| Authors Name: | Pravieen AS , Rajagopal C |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_213978
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| Published Paper Id: | IJNRD2403343 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | Accurate and efficient brain tumor classification is paramount for timely clinical diagnosis and effective treatment planning. In this groundbreaking research, we introduce an innovative Convolutional Neural Network (CNN) architecture, intricately integrated with customized preprocessing techniques, resulting in an exceptional classification accuracy of 98.5% on the challenging brH36 dataset. By harnessing the power of MRI scans and leveraging diverse datasets, our model not only expedites brain tumor assessments but also sets the stage for advanced classification methodologies. With the global incidence of brain tumors on the rise, the need for technology-driven diagnosis becomes increasingly evident, and CNNs emerge as pivotal tools in enhancing diagnostic precision. This study not only underscores the profound significance of CNN models but also transcends geographical boundaries, reducing the frequency of misdiagnoses, and ultimately empowering global healthcare. This paper offers a comprehensive exploration of our methodology, delving into the intricate details of data collection processes, model development strategies, and experimental findings. Moreover, it sheds light on the broader implications of deploying CNN models in the field of medical imaging. By contributing to the ongoing discourse on transformative healthcare technologies, this research aims to propel the adoption of CNN-based approaches, ushering in a new era of precise and efficient brain tumor classification for the benefit of healthcare professionals, patients, and society at large. |
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| Cite Article: | "Brain Tumor Detection Using Convolution Neural Network", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.d308-d315, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403343.pdf |
| Downloads: | 00023 |
| 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:IJNRD2403343 Registration ID: 213978 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: d308-d315 Country: Chengalpattu, Tamil Nadu, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403343 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403343 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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