Paper Title
Automated Brain Tumor Classification using Fusion Model of VGG19 – EfficientNetB0
Article Identifiers
Authors
GEETHA N , Tejashwini P S , Dr. Thriveni J
Keywords
Fusion, Transfer learning, EfficientNetB0, VGG16.
Abstract
The increasing mortality rate attributed to brain tumors, characterized by abnormal clusters of rapidly dividing cells in or around the brain, poses a growing concern. Early detection significantly improves survival prospects, underscoring the necessity of tools with automated assistance for prompt diagnosis. Magnetic resonance (MR) images play a crucial role in detecting brain tumors, with various deep learning algorithms like VGG19 and EfficientNetB0 being utilized. To capitalize on the strengths of these algorithms, a Fusion Model is employed, amalgamating their respective capabilities. This fusion model underwent testing on a challenging dataset comprising 7,023 MRI brain tumor images, yielding impressive results. It demonstrated 100% accuracy during training and 99% during testing, highlighting its efficacy in enhancing CNN performance for image classification on the Kaggle dataset Br35H.
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How To Cite (APA)
GEETHA N, Tejashwini P S, & Dr. Thriveni J (March-2024). Automated Brain Tumor Classification using Fusion Model of VGG19 – EfficientNetB0 . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), g524-g531. https://ijnrd.org/papers/IJNRD2403665.pdf
Issue
Volume 9 Issue 3, March-2024
Pages : g524-g531
Other Publication Details
Paper Reg. ID: IJNRD_216310
Published Paper Id: IJNRD2403665
Downloads: 000121978
Research Area: Computer Science & TechnologyÂ
Country: Bengaluru, Karnataka, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2403665.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403665
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
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