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

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research