Brain Tumor Detection with Ensemble of Convolutional Neural Networks and Vision Transformer
Yazarlar (2)
Arş. Gör. Mustafa Yurdakul Kirikkale Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Sinop Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/EICEEAI60672.2023.10590129 | ||
| Kongre Adı | 2nd Engineering International Conference on Electrical, Energy, and Artificial Intelligence(EICEEAI) 2023 | ||
| Kongre Tarihi | 27-12-2023 / 28-12-2023 | ||
| Basıldığı Ülke | Ürdün | Basıldığı Şehir | |
| Bildiri Linki | https://ieeexplore.ieee.org/document/10590129 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
Yapay Zeka
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| Özet |
| Brain tumors are recognized as one of the most lethal cancer types worldwide. Detecting brain tumors using medical imaging techniques is a challenging task due to their complex anatomical structures. Traditional methods rely on specialists meticulously examining MRI scan images. However, this approach is not only time-consuming but also carries a significant risk of error. Therefore, there is a need for more effective methods to detect brain tumors from MRI images. In this study, an ensemble model was proposed for classifying tumor types using MRI scans. Initially, sixteen well-known Convolutional Neural Network (CNN) models and four Vision Transformer (ViT) models were trained on the Brain Tumor Dataset, which contains 3264 MRI scan images. Subsequently, by combining the top three high-performing models, we achieved a robust classification performance. Experimental results demonstrate that our … |
| Anahtar Kelimeler |
| Brain Tumor | Classification | CNN | Detection | Vision Transformers |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 4 |
| Google Scholar | 10 |