Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (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)
The detection and classification of brain tumors through imaging techniques is a crucial task in medical diagnosis. Convolutional Neural Networks (CNN) have shown remarkable capabilities in image processing and classification tasks. This paper presents a CNN-based approach for classifying and detecting brain tumors using MRI images. We trained the model using a dataset of MRI brain scans, incorporating various types of tumors. The proposed model achieved a high accuracy in distinguishing between tumor and non-tumor images and demonstrated its capability to identify the tumor’s location effectively. These results suggest the viability of CNNs in clinical settings for early tumor detection and diagnosis.
"Image Classification & Detection of Brain Tumour", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c869-c872, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505300.pdf
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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