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Research Article

Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model

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  • @ARTICLE{10.4108/eetpht.10.6377,
        author={Disha Sushant Wankhede and Chetan J. Shelke and Virendra Kumar Shrivastava and Rathnakar Achary and Sachi Nandan Mohanty},
        title={Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2024},
        month={12},
        keywords={CNN, Brain Tumor, MRI, Transfer Learning, Inception-V3, CNN-AlexNet, VGG16, VGG19},
        doi={10.4108/eetpht.10.6377}
    }
    
  • Disha Sushant Wankhede
    Chetan J. Shelke
    Virendra Kumar Shrivastava
    Rathnakar Achary
    Sachi Nandan Mohanty
    Year: 2024
    Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model
    PHAT
    EAI
    DOI: 10.4108/eetpht.10.6377
Disha Sushant Wankhede1,*, Chetan J. Shelke1,*, Virendra Kumar Shrivastava1,*, Rathnakar Achary1,*, Sachi Nandan Mohanty2,*
  • 1: Alliance University
  • 2: Vellore Institute of Technology University
*Contact email: sdishaphd719@ced.alliance.edu.in, sdishaphd719@ced.alliance.edu.in, sdishaphd719@ced.alliance.edu.in, sdishaphd719@ced.alliance.edu.in, sdishaphd719@ced.alliance.edu.in

Abstract

INTRODUCTION: Brain tumors have become a major global health concern, characterized by the abnormal growth of brain cells that can negatively affect surrounding tissues. These cells can either be malignant (cancerous) or benign (non-cancerous), with their impact varying based on their location, size and type. OBJECTIVE: Early detection and classification of brain tumors are challenging due to their complex and variable structural makeup. Accurate early diagnosis is crucial to minimize mortality rates. METHOD: To address this challenge, researchers proposed an optimized model based on Convolutional Neural Networks (CNNs) with transfer learning, utilizing architectures like Inception-V3, AlexNet, VGG16, and VGG19. This study evaluates the performance of these adjusted CNN models for brain tumor identification and classification using MRI data. The TCGA-LGG and The TCIA, two well-known open-source datasets, were employed to assess the model's performance. The optimized CNN architecture leveraged pre-trained weights from large image datasets through transfer learning. RESULTS: The refined ResNet50-152 model demonstrated impressive performance metrics: for the non-tumor class, it achieved a precision of 0.98, recall of 0.95, F1 score of 0.93, and accuracy of 0.94; for the tumor class, it achieved a precision of 0.87, recall of 0.92, F1 score of 0.88, and accuracy of 0.96. CONCLUSION: These results indicate that the refined CNN model significantly improves accuracy in classifying brain tumors from MRI scans, showcasing its potential for enhancing early diagnosis and treatment planning.

Keywords
CNN, Brain Tumor, MRI, Transfer Learning, Inception-V3, CNN-AlexNet, VGG16, VGG19
Received
2024-12-04
Accepted
2024-12-04
Published
2024-12-04
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.10.6377

Copyright © 2024 D.S.Wankhede et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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