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AIMS Mathematics

2024, Volume 9, Issue 2: 4604-4631. doi: 10.3934/math.2024222
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Research article Special Issues

Classification of MRI brain tumors based on registration preprocessing and deep belief networks

  • 1.
    Department of Computer Science, College of Arts and Sciences at Tabarjal, Jouf University
  • 2.
    University of Sfax, MIRACL Laboratory ISIMS, Sakiet Ezzeit, Sfax, Tunisia
  • 3.
    College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
  • 4.
    STIH Laboratory, Sorbonne Université, Paris, France
  • 5.
    ReDCAD Laboratory, University of Sfax, Sfax, Tunisia
  • 6.
    Department of Information Systems, College of Computer and Information Sciences, Jouf University
  • 7.
    Department of Physics, College of Arts and Sciences at Tabarjal, Jouf University
  • Received: 27 October 2023 Revised: 29 December 2023 Accepted: 08 January 2024 Published: 18 January 2024

  • MSC : 62H30, 68T10, 62H35, 68T07, 68T10

  • In recent years, augmented reality has emerged as an emerging technology with huge potential in image-guided surgery, and in particular, its application in brain tumor surgery seems promising. Augmented reality can be divided into two parts: hardware and software. Further, artificial intelligence, and deep learning in particular, have attracted great interest from researchers in the medical field, especially for the diagnosis of brain tumors. In this paper, we focus on the software part of an augmented reality scenario. The main objective of this study was to develop a classification technique based on a deep belief network (DBN) and a softmax classifier to (1) distinguish a benign brain tumor from a malignant one by exploiting the spatial heterogeneity of cancer tumors and homologous anatomical structures, and (2) extract the brain tumor features. In this work, we developed three steps to explain our classification method. In the first step, a global affine transformation is preprocessed for registration to obtain the same or similar results for different locations (voxels, ROI). In the next step, an unsupervised DBN with unlabeled features is used for the learning process. The discriminative subsets of features obtained in the first two steps serve as input to the classifier and are used in the third step for evaluation by a hybrid system combining the DBN and a softmax classifier. For the evaluation, we used data from Harvard Medical School to train the DBN with softmax regression. The model performed well in the classification phase, achieving an improved accuracy of 97.2%.

    Citation: Karim Gasmi, Ahmed Kharrat, Lassaad Ben Ammar, Ibtihel Ben Ltaifa, Moez Krichen, Manel Mrabet, Hamoud Alshammari, Samia Yahyaoui, Kais Khaldi, Olfa Hrizi. Classification of MRI brain tumors based on registration preprocessing and deep belief networks[J]. AIMS Mathematics, 2024, 9(2): 4604-4631. doi: 10.3934/math.2024222

    Related Papers:

  • In recent years, augmented reality has emerged as an emerging technology with huge potential in image-guided surgery, and in particular, its application in brain tumor surgery seems promising. Augmented reality can be divided into two parts: hardware and software. Further, artificial intelligence, and deep learning in particular, have attracted great interest from researchers in the medical field, especially for the diagnosis of brain tumors. In this paper, we focus on the software part of an augmented reality scenario. The main objective of this study was to develop a classification technique based on a deep belief network (DBN) and a softmax classifier to (1) distinguish a benign brain tumor from a malignant one by exploiting the spatial heterogeneity of cancer tumors and homologous anatomical structures, and (2) extract the brain tumor features. In this work, we developed three steps to explain our classification method. In the first step, a global affine transformation is preprocessed for registration to obtain the same or similar results for different locations (voxels, ROI). In the next step, an unsupervised DBN with unlabeled features is used for the learning process. The discriminative subsets of features obtained in the first two steps serve as input to the classifier and are used in the third step for evaluation by a hybrid system combining the DBN and a softmax classifier. For the evaluation, we used data from Harvard Medical School to train the DBN with softmax regression. The model performed well in the classification phase, achieving an improved accuracy of 97.2%.


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