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)
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.
Keywords:
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
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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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