A New Approach for Effective Medical Deepfake Detection in Medical Images
Yazarlar (3)
Prof. Dr. Mehmet Karaköse Firat Üniversitesi, Türkiye
Dr. Öğr. Üyesi Hasan Yetiş Firat Üniversitesi, Türkiye
Arş. Gör. Mert ÇEÇEN Kastamonu Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | IEEE Access (Q2) | ||
| Dergi ISSN | 2169-3536 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2024 |
| Cilt / Sayı / Sayfa | 12 / 1 / 52205–52214 | DOI | 10.1109/ACCESS.2024.3386644 |
| Makale Linki | https://doi.org/10.1109/access.2024.3386644 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
Yapay Zeka
Makine Öğrenmesi
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| Özet |
| In today's world, deepfake technology is being used to generate fake images, sounds, and videos from real images and sounds using deep learning and artificial intelligence techniques. It is possible to manipulate medical images with this technology. The manipulation of medical images can lead to incorrect diagnoses by medical professionals, disrupting the functioning of hospitals. As a result of these disruptions, hospitals may experience significant financial and life-threatening problems. In this study, it is aimed to obtain an effective deep learning-based method to detect manipulated medical images. Initially, two distinct datasets are created which contain Knee Osteoarthritis X-ray and lung CT scans. Data pre-processing and augmentation methods are applied for data standardization and variation. The instances in datasets are labeled as real or fake. The medical deepfake distinguish ability of YoloV3 ... |
| Anahtar Kelimeler |
| convolutional neural networks | deep learning | Medical deepfake image detection | YOLO |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 18 |
| Scopus | 49 |
| Google Scholar | 68 |