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Benchmarking Deep Learning Models for Automated MRI-based Brain Tumor Detection: In-Depth Analysis of CNN, VGG16, VGG19, ResNet-50, MobileNet, and InceptionV3

by Sarah Pendhari, Nazneen Pendhari, Sonal Shroff
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 50
Year of Publication: 2024
Authors: Sarah Pendhari, Nazneen Pendhari, Sonal Shroff
10.5120/ijca2024924233

Sarah Pendhari, Nazneen Pendhari, Sonal Shroff . Benchmarking Deep Learning Models for Automated MRI-based Brain Tumor Detection: In-Depth Analysis of CNN, VGG16, VGG19, ResNet-50, MobileNet, and InceptionV3. International Journal of Computer Applications. 186, 50 ( Nov 2024), 31-35. DOI=10.5120/ijca2024924233

@article{ 10.5120/ijca2024924233,
author = { Sarah Pendhari, Nazneen Pendhari, Sonal Shroff },
title = { Benchmarking Deep Learning Models for Automated MRI-based Brain Tumor Detection: In-Depth Analysis of CNN, VGG16, VGG19, ResNet-50, MobileNet, and InceptionV3 },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2024 },
volume = { 186 },
number = { 50 },
month = { Nov },
year = { 2024 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number50/benchmarking-deep-learning-models-for-automated-mri-based-brain-tumor-detection-in-depth-analysis-of-cnn-vgg16-vgg19-resnet-50-mobilenet-and-inceptionv3/ },
doi = { 10.5120/ijca2024924233 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-11-27T00:39:39.492138+05:30
%A Sarah Pendhari
%A Nazneen Pendhari
%A Sonal Shroff
%T Benchmarking Deep Learning Models for Automated MRI-based Brain Tumor Detection: In-Depth Analysis of CNN, VGG16, VGG19, ResNet-50, MobileNet, and InceptionV3
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 50
%P 31-35
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The early and precise diagnosis of brain tumors is paramount in the medical field, significantly impacting treatment efficacy and patient survival rates. Magnetic Resonance Imaging (MRI), a non-intrusive diagnostic tool, is extensively utilized for identifying brain tumors, eliminating the need for invasive biopsies. However, the manual interpretation of MRI scans is a challenging and laborious task due to the voluminous and complex nature of the three-dimensional images it generates. So this study harnesses the power of advanced state-of-the-art deep learning models – Convolutional Neural Network (CNN), VGG16, VGG19, ResNet-50, Mobile Net and InceptionV3 – to automate and enhance the accuracy of brain tumor detection from MRI data. The findings demonstrate a marked improvement in the detection and accuracy of brain tumors, showcasing the potential of deep learning in revolutionizing medical imaging diagnostics with accuracy scores of CNN being 97.55%, VGG16 being 97.96%, VGG19 being 97.55%, ResNet-50 being 90.20%, MobileNetV1 being 96.73% andInceptionV3 being 97.55%. This innovation not only streamlines the diagnostic process but also opens new avenues for early intervention strategies, ultimately contributing to improved patient outcomes in the fight against one of the most daunting health challenges of all time.

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Index Terms

Computer Science
Information Sciences
Computer Vision
Image Processing
Brain Tumor Detection

Keywords

Medical Imaging Brain Tumor Detection Automated Diagnosis Convolutional Neural Network (CNN) Deep Learning Visual Geometry Group (VGG16 VGG19) Residual Networks (ResNet-50) MobileNet InceptionV3

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