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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Introduction:

Recent advances in deep learning have aided the well-being business in Medical Imaging of numerous disorders like brain tumours, a serious malignancy caused by unregulated and aberrant cell portioning. The most frequent and widely used machine learning algorithm for visual learning and image identification is CNN.

Methods:

In this article, the convolutional neural network (CNN) technique is used. Augmentation of data and processing of images is used to classify scan imagery of brain MRI as malignant or benign. The performance of the proposed CNN model is compared with pre-trained models: VGG-16, ResNet-50, and Inceptionv3 using the technique which is transfer learning.

Results:

Even though the experiment was conducted on a relatively limited dataset, the experimental results reveal that the suggested scratched CNN model accuracy achieved is 94%, VGG-16 was extremely effective and had a very low complexity rate with an accuracy of 90%, whereas ResNet- 50 reached 86% and Inception v3 obtained 64% accuracy.

Conclusion:

When compared to previous pre-trained models, the suggested model consumes significantly less processing resources and achieves significantly higher accuracy outcomes and a reduction in losses.

© 2024 The Author(s). Published by Bentham Open.
© 2024 The Author(s). Published by Bentham Open. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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/content/journals/cmir/10.2174/1573405620666230327124902
2023-07-07
2025-01-18

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/content/journals/cmir/10.2174/1573405620666230327124902
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Classification of Brain Tumours in MRI Images using a Convolutional Neural Network
Curr. Med. Imaging 20, E270323214998 (2024); https://doi.org/10.2174/1573405620666230327124902
/content/journals/cmir/10.2174/1573405620666230327124902
/content/journals/cmir/10.2174/1573405620666230327124902
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  • Article Type:     Research Article
Keyword(s): Brain tumour; Classification; Deep learning; Medical imaging; National brain tumor; Transfer learning

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