Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images
A brain tumor is a collection of abnormal cells formed as a result of uncontrolled cell division. If tumors are not diagnosed in a timely and accurate manner, they can cause fatal consequences. One of the commonly used techniques to detect brain tumors is magnetic resonance imaging (MRI). MRI provid...
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Istanbul University Press
2021-06-01
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| Series: | Acta Infologica |
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| author | Onur Sevli |
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| collection | DOAJ |
| description | A brain tumor is a collection of abnormal cells formed as a result of uncontrolled cell division. If tumors are not diagnosed in a timely and accurate manner, they can cause fatal consequences. One of the commonly used techniques to detect brain tumors is magnetic resonance imaging (MRI). MRI provides easy detection of abnormalities in the brain with its high resolution. MR images have traditionally been studied and interpreted by radiologists. However, with the development of technology, it becomes more difficult to interpret large amounts of data produced in reasonable periods. Therefore, the development of computerized semi-automatic or automatic methods has become an important research topic. Machine learning methods that can predict by learning from data are widely used in this field. However, the extraction of image features requires special engineering in the machine learning process. Deep learning, a sub-branch of machine learning, allows us to automatically discover the complex hierarchy in the data and eliminates the limitations of machine learning. Transfer learning is to transfer the knowledge of a pre-trained neural network to a similar model in case of limited training data or the goal of reducing the workload. In this study, the performance of the pre-trained Vgg-16, ResNet50, Inception v3 models in classifying 253 brain MR images were evaluated. The Vgg-16 model showed the highest success with 94.42% accuracy, 83.86% recall, 100% precision and 91.22% F1 score. This was followed by the ResNet50 model with an accuracy of 82.49%.The findings obtained in this study were compared with similar studies in the literature and it was found that it showed higher success than most studies. |
| format | Article |
| id | doaj-art-a985ae37751d4f0ca06f7b809a808f21 |
| institution | DOAJ |
| issn | 2602-3563 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Istanbul University Press |
| record_format | Article |
| series | Acta Infologica |
| spelling | doaj-art-a985ae37751d4f0ca06f7b809a808f212025-08-20T03:10:53ZengIstanbul University PressActa Infologica2602-35632021-06-015114115410.26650/acin.880918123456Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI ImagesOnur Sevli0https://orcid.org/0000-0002-8933-8395Burdur Mehmet Akif Ersoy Üniversitesi, Burdur, TurkiyeA brain tumor is a collection of abnormal cells formed as a result of uncontrolled cell division. If tumors are not diagnosed in a timely and accurate manner, they can cause fatal consequences. One of the commonly used techniques to detect brain tumors is magnetic resonance imaging (MRI). MRI provides easy detection of abnormalities in the brain with its high resolution. MR images have traditionally been studied and interpreted by radiologists. However, with the development of technology, it becomes more difficult to interpret large amounts of data produced in reasonable periods. Therefore, the development of computerized semi-automatic or automatic methods has become an important research topic. Machine learning methods that can predict by learning from data are widely used in this field. However, the extraction of image features requires special engineering in the machine learning process. Deep learning, a sub-branch of machine learning, allows us to automatically discover the complex hierarchy in the data and eliminates the limitations of machine learning. Transfer learning is to transfer the knowledge of a pre-trained neural network to a similar model in case of limited training data or the goal of reducing the workload. In this study, the performance of the pre-trained Vgg-16, ResNet50, Inception v3 models in classifying 253 brain MR images were evaluated. The Vgg-16 model showed the highest success with 94.42% accuracy, 83.86% recall, 100% precision and 91.22% F1 score. This was followed by the ResNet50 model with an accuracy of 82.49%.The findings obtained in this study were compared with similar studies in the literature and it was found that it showed higher success than most studies.https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/99DD9C496BF14E44859851B33E49A006brain mri classificationtransfer learningconvolutional neural networks |
| spellingShingle | Onur Sevli Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images Acta Infologica brain mri classification transfer learning convolutional neural networks |
| title | Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images |
| title_full | Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images |
| title_fullStr | Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images |
| title_full_unstemmed | Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images |
| title_short | Performance Comparison of Different Pre-Trained Deep Learning Models in Classifying Brain MRI Images |
| title_sort | performance comparison of different pre trained deep learning models in classifying brain mri images |
| topic | brain mri classification transfer learning convolutional neural networks |
| url | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/99DD9C496BF14E44859851B33E49A006 |
| work_keys_str_mv | AT onursevli performancecomparisonofdifferentpretraineddeeplearningmodelsinclassifyingbrainmriimages |