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A Novel Approach to Predict Brain Cancerous Tumor Using Transfer Learning.

Mohammad Monirujjaman Khan1, Atiyea Sharmeen Omee1, Tahia Tazin1

  • 1Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka 1229, Bangladesh.

Computational and Mathematical Methods in Medicine
|June 30, 2022
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Summary
This summary is machine-generated.

Convolutional neural networks (CNNs) significantly improve brain tumor detection accuracy. MobileNetV2 achieved 97% accuracy, enabling earlier diagnosis and better treatment outcomes for this deadly cancer.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Brain tumors are a leading cause of cancer mortality with poor survival rates.
  • Manual segmentation and classification of brain tumors are challenging, time-consuming, and prone to inaccuracies.
  • Distinguishing malignant tumors from normal brain tissue is difficult due to subtle visual similarities.

Purpose of the Study:

  • To investigate the efficacy of convolutional neural networks (CNNs) for image-based diagnosis of brain tumors.
  • To enhance the accuracy and reliability of brain cancer diagnosis using deep learning.
  • To leverage transfer learning for improved classification performance.

Main Methods:

  • Utilized CT and X-ray imaging for brain tumor identification.
  • Employed Python and Google Colab for the experimental setup.
  • Extracted deep features using pre-trained CNN models: VGG19 and MobileNetV2.
  • Applied transfer learning to optimize model accuracy.

Main Results:

  • Achieved a classification accuracy of 97% using the MobileNetV2 model.
  • Obtained a classification accuracy of 91% using the VGG19 model.
  • Demonstrated the effectiveness of deep learning models in brain tumor classification.

Conclusions:

  • CNNs, particularly MobileNetV2, offer a highly accurate approach for brain tumor diagnosis.
  • Early detection through advanced imaging analysis can prevent severe neurological deficits.
  • This research supports the integration of AI in oncology for improved patient outcomes.