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Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

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dc.contributor.authorYılmaz, Vadi Su
dc.contributor.authorAkdağ, Metehan
dc.contributor.authorDalveren, Yaser
dc.contributor.authorDoruk, Reşat Özgür
dc.contributor.authorKara, Ali
dc.contributor.authorSoylu, Ahmet
dc.date.accessioned2023-12-11T11:53:45Z
dc.date.available2023-12-11T11:53:45Z
dc.date.issued2023-02-09
dc.descriptionOpen Access, Published by Diagnostics, https://doi.org/10.3390/diagnostics13040651, Vadi Su Yilmaz, Yaser Dalveren, Resat Ozgur Doruk, Department of Electrical and Electronics Engineering, Atilim University, Kizilcasar Mahallesi, Incek Golbasi, Ankara 06830, Turkey, Metehan Akdag, Fonet Information Technologies, Kizilirmak Mahallesi, Cukurambar Cankaya, Ankara 06520, Turkey, Ali Kara, Department of Electrical and Electronics Engineering, Gazi University, Eti Mahallesi, Yukselis Sokak, Maltepe, Ankara 06570, Turkey, Ahmet Soylu, Department of Computer Science, OsloMet—Oslo Metropolitan University, Pilestredet 35, Oslo 0167, Norway.
dc.description.abstractBrain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.
dc.identifier.citationhttp://hdl.handle.net/20.500.14411/1868
dc.identifier.issn2075-4418
dc.identifier.urihttps://doi.org/10.3390/diagnostics13040651
dc.language.isoen
dc.publisherDiagnostics
dc.relation.ispartofseries13; 4
dc.subjectBrain tumor detection; glioma; deep learning; U-Net; V-Net; MATLAB; Python; performance assessment
dc.titleInvestigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images
dc.typeArticle
dspace.entity.typeArticle

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