Multi-Class brain normality and abnormality diagnosis using modified Faster R-CNN
Yazarlar (5)
Dr. Öğr. Üyesi Kübra Uyar Selçuk Üniversitesi, Türkiye
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
Prof. Dr. Erkan Ülker Konya Technical University, Türkiye
Prof. Dr. Mehmet Öztürk Selçuk Üniversitesi, Türkiye
Hüseyin Kasap
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal of Medical Informatics (Q1) | ||
| Dergi ISSN | 1386-5056 Dergi Bilgileri (2021) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 11-2021 |
| Cilt / Sayı / Sayfa | 155 / 1 / 104576–0 | DOI | 10.1016/j.ijmedinf.2021.104576 |
| Makale Linki | http://dx.doi.org/10.1016/j.ijmedinf.2021.104576 | ||
| UAK Araştırma Alanları |
Yapay Zeka
|
||
| Özet |
| Background and ObjectiveThe detection and analysis of brain disorders through medical imaging techniques are extremely important to get treatment on time and sustain a healthy lifestyle. Disorders cause permanent brain damage and alleviate the lifespan. Moreover, the classification of large volumes of medical image data manually by medicine experts is tiring, time-consuming, and prone to errors. This study aims to diagnose brain normality and abnormalities using a novel ResNet50 modified Faster Regions with Convolutional Neural Network(R-CNN) model. The classification task is performed into multiple classes which are hemorrhage, hydrocephalus, and normal. The proposed model both determines the borders of the normal/abnormal parts and classifies them with the highest accuracy.MethodsTo provide a comprehensive performance analysis in the classification problem, Machine Learning(ML) and … |
| Anahtar Kelimeler |
| Brain CT | CNN | Detection | Faster R-CNN | Machine Learning |
Science Direct
Multi-Class brain normality and abnormality diagnosis using modified Faster R-CNN
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 11 |
| Scopus | 16 |
| Google Scholar | 18 |