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Automatic brain tumor detection using CNN transfer learning approach.

Vinayak K Bairagi1, Pratima Purushottam Gumaste2, Seema H Rajput3

  • 1Department of Electronics and Telecommunication, AISSMS Institute of Information Technology, Pune, India. bairagi1@gmail.com.

Medical & Biological Engineering & Computing
|March 23, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a convolutional neural network (CNN) approach for automatic brain tumor detection using MRI images. The proposed CNN Alexnet model achieved 98.67% accuracy, offering a reliable solution for identifying cancerous tissues.

Keywords:
Alexnet architectureBrain tumorMRINeural networksTransfer learningVGG-16 architecture

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuro-oncology

Background:

  • Brain tumor detection is critical for patient survival but challenging due to tumor variability and manual analysis limitations.
  • Manual extraction of tumor information from medical imaging data is time-consuming and complex.
  • Automated systems are essential for accurate and efficient brain tumor identification.

Purpose of the Study:

  • To propose and evaluate a convolutional neural network (CNN) based automated system for brain tumor recognition from MRI images.
  • To compare the performance of various CNN architectures, including Alexnet, VGG-16, GooGLeNet, and RNN.
  • To optimize hyperparameters for Alexnet and VGG-16 architectures for improved detection accuracy.

Main Methods:

  • Utilized magnetic resonance imaging (MRI) datasets (BRATS 2013, BRATS 2015, OPEN I) comprising 621 images for training and validation.
  • Implemented and compared multiple CNN architectures for image classification into tumorous and non-tumorous categories.
  • Focused on hyperparameter tuning for Alexnet and VGG-16 models to enhance performance.

Main Results:

  • The CNN Alexnet architecture achieved a high accuracy of 98.67% in detecting brain tumors.
  • Performance comparison indicated Alexnet's superiority among the explored architectures for this task.
  • The system demonstrated effective classification of MRI images into tumorous and non-tumorous classes.

Conclusions:

  • Convolutional neural networks offer a reliable and automated approach for brain tumor detection using MRI.
  • The developed CNN Alexnet model provides a highly accurate method for identifying brain tumors.
  • This automated system can significantly aid medical experts in timely diagnosis and treatment planning.