Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images

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dc.authorid0000-0002-0060-1880en_US
dc.contributor.authorTürk, Ömer
dc.contributor.authorOzhan, Davut
dc.contributor.authorAcar, Emrullah
dc.contributor.authorAkinci, Tahir Cetin
dc.contributor.authorYilmaz, Musa
dc.date.accessioned2023-01-18T11:31:01Z
dc.date.available2023-01-18T11:31:01Z
dc.date.issued2022en_US
dc.departmentMAÜ, Meslek Yüksekokulları, Mardin Meslek Yüksekokulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractToday, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal cells begin to form in masses in the brain regions. Nowadays, many different techniques are employed to detect these tumor masses, and the most common of these techniques is Magnetic Resonance Imaging (MRI). In this study, it is aimed to automatically detect brain tumors with the help of ensemble deep learning architectures (ResNet50, VGG19, InceptionV3 and MobileNet) and Class Activation Maps (CAMs) indicators by employing MRI images. The proposed system was implemented in three stages. In the first stage, it was determined whether there was a tumor in the MR images (Binary Approach). In the second stage, different tumor types (Normal, Glioma Tumor, Meningioma Tumor, Pituitary Tumor) were detected from MR images (Multi-class Approach). In the last stage, CAMs of each tumor group were created as an alternative tool to facilitate the work of specialists in tumor detection. The results showed that the overall accuracy of the binary approach was calculated as 100% on the ResNet50, InceptionV3 and MobileNet architectures, and 99.71% on the VGG19 architecture. Moreover, the accuracy values of 96.45% with ResNet50, 93.40% with VGG19, 85.03% with InceptionV3 and 89.34% with MobileNet architectures were obtained in the multi-class approach.en_US
dc.identifier.citationTurk, O., Ozhan, D., Acar, E., Akinci, T. C., & Yilmaz, M. (2022). Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images. Zeitschrift für Medizinische Physik.en_US
dc.identifier.doi10.1016/j.zemedi.2022.11.010en_US
dc.identifier.pmid36593139en_US
dc.identifier.scopus2-s2.0-85146034605en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1016/j.zemedi.2022.11.010
dc.identifier.urihttps://hdl.handle.net/20.500.12514/3342
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherScienceDirecten_US
dc.relation.ispartofZeitschrift für Medizinische Physiken_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEnsemble Deep LearningClass Activation MapsResNet50VGG19InceptionV3MobileNetTumor TypesMRIen_US
dc.titleAutomatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance imagesen_US
dc.typeArticleen_US

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