Comparison of deep learning models for brain tumor classification using MRI images
| dc.contributor.author | Cinar, Necip | |
| dc.contributor.author | Kaya, Buket | |
| dc.contributor.author | Kaya, Mehmet | |
| dc.date.accessioned | 2024-04-24T17:11:22Z | |
| dc.date.available | 2024-04-24T17:11:22Z | |
| dc.date.issued | 2022 | |
| dc.department | Dicle Üniversitesi | en_US |
| dc.description | International Conference on Decision Aid Sciences and Applications (DASA) -- MAR 23-25, 2022 -- Chiangrai, THAILAND | en_US |
| dc.description.abstract | Brain tumor is a type of cancer that can occur in humans, sometimes with fatal consequences, or seriously affect quality of life. Detection of brain tumors using deep learning methods is a very positive development for experts. Deep learning methods enable experts to perform tumor detection and treatment easier and faster. There are many methods are used to detect tumors from brain MRI images. Among these methods, deep learning methods have made a significant improvement over other methods. In this study, it is aimed to compare the models used for tumor detection from brain MRI images within the scope of deep learning methods. For this reason, the five most commonly used convolutional neural networks for brain tumor classification are discussed. VGG19, DenseNet169, AlexNet, InceptionV3 and ResNet101 models, which are Convolutional Neural Network (CNN) architectures, were used. MR images, which underwent the same dataset and preprocessing processes, were trained with these models with the same hyper-parameters. As a result of the study, the ResNet101 model obtained the highest accuracy rate with an accuracy value of 98,6%. In addition, the VGG19 model showed a very high accuracy rate of 97.2%. Other models have accuracy values of InceptionV3 94.3%, DenseNet169 92.8%, and AlexNet 89.5%, respectively. This low success rate reveals that the architectures used in these models are not suitable for studies on MR images compared to other architectures. As a result, it has been concluded that the use of ResNet architecture for tumor detection from brain MR images is more advantageous than other models. | en_US |
| dc.identifier.doi | 10.1109/DASA54658.2022.9765250 | |
| dc.identifier.endpage | 1385 | en_US |
| dc.identifier.isbn | 978-1-6654-9501-1 | |
| dc.identifier.scopus | 2-s2.0-85130166115 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.startpage | 1382 | en_US |
| dc.identifier.uri | https://doi.org/10.1109/DASA54658.2022.9765250 | |
| dc.identifier.uri | https://hdl.handle.net/11468/17450 | |
| dc.identifier.wos | WOS:000839386600069 | |
| dc.identifier.wosquality | N/A | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | en_US |
| dc.publisher | Ieee | en_US |
| dc.relation.ispartof | 2022 International Conference on Decision Aid Sciences and Applications (Dasa) | |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Transfer Learning | en_US |
| dc.subject | Convolutional Neural Network | en_US |
| dc.subject | Brain Tumor Classification | en_US |
| dc.subject | Brain Mri Images | en_US |
| dc.title | Comparison of deep learning models for brain tumor classification using MRI images | en_US |
| dc.title | Comparison of deep learning models for brain tumor classification using MRI images | |
| dc.type | Conference Object | en_US |
