Classification of MRI brain tumors based on registration preprocessing and deep belief networks
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, a...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-01-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.2024222?viewType=HTML |
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| author | Karim Gasmi Ahmed Kharrat Lassaad Ben Ammar Ibtihel Ben Ltaifa Moez Krichen Manel Mrabet Hamoud Alshammari Samia Yahyaoui Kais Khaldi Olfa Hrizi |
| author_facet | Karim Gasmi Ahmed Kharrat Lassaad Ben Ammar Ibtihel Ben Ltaifa Moez Krichen Manel Mrabet Hamoud Alshammari Samia Yahyaoui Kais Khaldi Olfa Hrizi |
| author_sort | Karim Gasmi |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-08T05:33:42Z |
| format | Article |
| id | doaj.art-269903348d5a4a31889555f3c2fd2b4b |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-03-08T05:33:42Z |
| publishDate | 2024-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-269903348d5a4a31889555f3c2fd2b4b2024-02-06T01:31:39ZengAIMS PressAIMS Mathematics2473-69882024-01-01924604463110.3934/math.2024222Classification of MRI brain tumors based on registration preprocessing and deep belief networksKarim Gasmi0Ahmed Kharrat1Lassaad Ben Ammar2Ibtihel Ben Ltaifa 3Moez Krichen 4Manel Mrabet5Hamoud Alshammari6 Samia Yahyaoui7Kais Khaldi8Olfa Hrizi 91. Department of Computer Science, College of Arts and Sciences at Tabarjal, Jouf University2. University of Sfax, MIRACL Laboratory ISIMS, Sakiet Ezzeit, Sfax, Tunisia3. College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia4. STIH Laboratory, Sorbonne Université, Paris, France5. ReDCAD Laboratory, University of Sfax, Sfax, Tunisia3. College of Sciences and Humanities, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia6. Department of Information Systems, College of Computer and Information Sciences, Jouf University7. Department of Physics, College of Arts and Sciences at Tabarjal, Jouf University1. Department of Computer Science, College of Arts and Sciences at Tabarjal, Jouf University1. Department of Computer Science, College of Arts and Sciences at Tabarjal, Jouf UniversityIn 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%.https://www.aimspress.com/article/doi/10.3934/math.2024222?viewType=HTMLbrain tumor classificationaugmented realitydeep belief networksmagnetic resonance imagingregistrationsoftmax |
| spellingShingle | 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 AIMS Mathematics brain tumor classification augmented reality deep belief networks magnetic resonance imaging registration softmax |
| title | Classification of MRI brain tumors based on registration preprocessing and deep belief networks |
| title_full | Classification of MRI brain tumors based on registration preprocessing and deep belief networks |
| title_fullStr | Classification of MRI brain tumors based on registration preprocessing and deep belief networks |
| title_full_unstemmed | Classification of MRI brain tumors based on registration preprocessing and deep belief networks |
| title_short | Classification of MRI brain tumors based on registration preprocessing and deep belief networks |
| title_sort | classification of mri brain tumors based on registration preprocessing and deep belief networks |
| topic | brain tumor classification augmented reality deep belief networks magnetic resonance imaging registration softmax |
| url | https://www.aimspress.com/article/doi/10.3934/math.2024222?viewType=HTML |
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