Brain Tumor Detection From MRI İmages With Using Proposed Deep Learning Model: The Partial Correlation-Based Channel Selection

dc.authorid110138en_US
dc.contributor.authorYılmaz, Atınç
dc.date.accessioned2021-12-21T17:16:58Z
dc.date.available2021-12-21T17:16:58Z
dc.date.issued2021
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractA brain tumor is an abnormal growth of a mass or cell in the brain. Early diagnosis of the tumor significantly increases the chances of successful treatment. Artificial intelligence-based systems can detect the tumor in early stages. In this way, it could be possible to detect a tumor and resolve this problem that may endanger human life early. In the study, the partial correlation-based channel selection formula was presented that allowed the selection of the most prominent feature that differs from the other studies in the literature. Additionally, the multi-channel convolution structure was proposed for the feature network phase of the Faster R-CNN architecture. In the proposed model, the most prominent features were obtained from the multi-channel selection structure in the feature network phase with the channel selection formula in the channel selection layer. The architecture was applied for the early detection of possible brain tumors, which are a severe risk for human life. Within the present study, the brain tumor was classified applying the proposed multi-channel Faster R-CNN based model with three different open-access datasets. VGG-16, faster region-based convolutional neural network (Faster R-CNN), DenseNet-201, Resnet-50, and SRN models, which are popular deep learning architectures, were applied to the same problem to compare the results and demonstrate the efficiency of the proposed model. Accuracy, sensitivity, and processing times of the applied methods were measured to demonstrate the models' performance and efficiency. As a result, the highest accuracy rates were obtained using the proposed model as 98.31%, 99.6%, and 99.8% for three datasets. In addition, it was compared with related studies in the literature to demonstrate the proposed model's applicability. The proposed model's accuracy and performance proved to be higher than in the other studies.en_US
dc.identifier.citationTurk J Elec Eng & Comp Sci (2021) 29: 2615 – 2633en_US
dc.identifier.doi10.3906/elk-2103-37
dc.identifier.issn2076-3417)
dc.identifier.scopus2-s2.0-85117108470en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.trdizinid526643en_US
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/526643
dc.identifier.urihttps://doi.org/10.3906/elk-2103-37
dc.identifier.wosWOS:000706716100003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectArtificial intelligenceen_US
dc.subjectProposed channel selection layeren_US
dc.subjectPartial correlationen_US
dc.subjectDeep learningen_US
dc.subjectFasster R-CNNen_US
dc.subjectBrain tumoren_US
dc.titleBrain Tumor Detection From MRI İmages With Using Proposed Deep Learning Model: The Partial Correlation-Based Channel Selectionen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Yükleniyor...
Küçük Resim
İsim:
07 Brain tumor detection from MRI images with using proposed deep learning.pdf
Boyut:
4.49 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama: