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Brain Tumor Detection and Classification Using Cycle Generative Adversarial Networks.

Rajeev Kumar Gupta1, Santosh Bharti2, Nilesh Kunhare3

  • 1Pandit Deendayal Energy University, Gandhinagar, India. Rajeevmanit12276@gmail.com.

Interdisciplinary Sciences, Computational Life Sciences
|February 9, 2022
PubMed
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This study introduces an advanced machine learning method for rapid and accurate brain tumor detection and staging using MRI scans. The new approach achieves high accuracy, offering a faster, non-invasive alternative to traditional biopsies.

Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in oncology
  • Neuroscience

Background:

  • Brain cancer is a leading cause of death, with traditional biopsy methods being invasive and time-consuming.
  • The need for rapid, non-invasive diagnostic tools for brain tumors is critical due to fast tumor growth.
  • Machine learning and deep learning show significant promise in cancer detection and classification.

Purpose of the Study:

  • To develop an automated, non-invasive system for detecting and staging brain tumors using MRI.
  • To improve the speed and precision of brain cancer diagnosis.
  • To address the challenge of small datasets in medical imaging through data augmentation.

Main Methods:

  • An ensemble method combining a modified InceptionResNetV2 model for tumor detection and a hybrid InceptionResNetV2/Random Forest Tree model for staging.
Keywords:
Brain tumorCNNCyclic GANData augmentationDeep learningInceptionResNetMRI image

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  • Utilized Cyclic Generative Adversarial Networks (C-GAN) to augment a small dataset.
  • Employed brain Magnetic Resonance Imaging (MRI) as the primary data source.

    • Main Results:

    • The proposed model achieved 99% accuracy for brain tumor detection.
    • The classification model accurately determined tumor stages (glioma, meningioma, pituitary) with 98% accuracy.
    • Data augmentation using C-GAN effectively addressed the limited dataset size.

    Conclusions:

    • The developed ensemble deep learning method provides a highly accurate and efficient approach for brain tumor detection and staging.
    • This non-invasive technique offers a significant advancement over traditional biopsy methods.
    • The study highlights the potential of AI in revolutionizing neuro-oncology diagnostics.