International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 5 September-October 2024 Submit your research before last 3 days of October to publish your research paper in the issue of September-October.

Detecting Subtypes of Brain Tumors using Resnet50 with Transfer Learning

Author(s) P. Narasimha, J. Likhith Chowdary, J. Vijay Kumar, K. Trivenu, Shaik Mulla Almas
Country India
Abstract To classify a Brain Tumor dataset consisting of 3064 T1 weighted contrast-enhanced brain MR (Magnetic Resonance) images, we used the CNN (Convolutional Neural Network) model ResNet50 Neural Network, one of the most popular deep learning architectures, in the proposed framework. We graded (classified) the brain tumors into three classes (Glioma, Meningioma, and Pituitary Tumor). Additionally, we will apply Transfer Learning to reduce the amount of data needed, improve neural network performance (most of the time), and save training time. Using a refined ResNet50 Neural Network architecture, our Brain Tumor Detector achieves over 95% accuracy by using the technique of Transfer Learning.
Keywords CNN, Deep Learning, ResNet50, Transfer Learning, Brain Tumors, Magnetic Resonance Imaging, Brain Tumor Detection
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-25
Cite This Detecting Subtypes of Brain Tumors using Resnet50 with Transfer Learning - P. Narasimha, J. Likhith Chowdary, J. Vijay Kumar, K. Trivenu, Shaik Mulla Almas - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.15579
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.15579
Short DOI https://doi.org/gtn3v2

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