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Grading of gliomas using transfer learning on MRI images

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objective

Despite the critical role of Magnetic Resonance Imaging (MRI) in the diagnosis of brain tumours, there are still many pitfalls in the exact grading of them, in particular, gliomas. In this regard, it was aimed to examine the potential of Transfer Learning (TL) and Machine Learning (ML) algorithms in the accurate grading of gliomas on MRI images.

Materials and methods

Dataset has included four types of axial MRI images of glioma brain tumours with grades I–IV: T1-weighted, T2-weighted, FLAIR, and T1-weighted Contrast-Enhanced (T1-CE). Images were resized, normalized, and randomly split into training, validation, and test sets. ImageNet pre-trained Convolutional Neural Networks (CNNs) were utilized for feature extraction and classification, using Adam and SGD optimizers. Logistic Regression (LR) and Support Vector Machine (SVM) methods were also implemented for classification instead of Fully Connected (FC) layers taking advantage of features extracted by each CNN.

Results

Evaluation metrics were computed to find the model with the best performance, and the highest overall accuracy of 99.38% was achieved for the model containing an SVM classifier and features extracted by pre-trained VGG-16.

Discussion

It was demonstrated that developing Computer-aided Diagnosis (CAD) systems using pre-trained CNNs and classification algorithms is a functional approach to automatically specify the grade of glioma brain tumours in MRI images. Using these models is an excellent alternative to invasive methods and helps doctors diagnose more accurately before treatment.

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Funding

No funding was received to assist with the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Oktay Fasihi Shirehjini & Farshid Babapour Mofrad

  2. Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mohammadreza Shahmohammadi

  3. Department of Medical Genetics, Applied Biophotonics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Fatemeh Karami

Authors
  1. Oktay Fasihi Shirehjini
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  2. Farshid Babapour Mofrad
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  3. Mohammadreza Shahmohammadi
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  4. Fatemeh Karami
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Correspondence to Farshid Babapour Mofrad.

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Fasihi Shirehjini, O., Babapour Mofrad, F., Shahmohammadi, M. et al. Grading of gliomas using transfer learning on MRI images. Magn Reson Mater Phy 36, 43–53 (2023). https://doi.org/10.1007/s10334-022-01046-y

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  • DOI: https://doi.org/10.1007/s10334-022-01046-y

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