| Yazarlar |
Doç. Dr. Ali Berkan URAL
Kafkas Üniversitesi, Türkiye |
| Özet |
| A sudden outbreak of Monkeypox disease has been reported recently in up to 70 countries so far and the spreading rate may be seen significantly around the world. The clinical aspects of Monkeypox have been reported that this disease looks like similar in many attributes when comparing some specific skin lesions as Chickenpox, Measles etc. These similarities make Monkeypox diagnosing and detecting difficult for doctors, clinicians or professionals by examining the visual appearance of the lesion on the skin. In addition, there has been a problem with the lack of detailed information about ultimate diagnosing of novel Monkeypox disease. It is also important that by the success of the studies about AI, Machine Learning and Deep Learning models in COVID-19 detection, the community has begun to give importance to detect Monkeypox via comprehensive AI methods from digital skin images. Moreover, in this paper, we develop a larger dataset to study and analyze the feasibility of common Artificial Intelligence based Deep Learning methods on skin images for Monkeypox detection. Our study has shown that Deep Learning models have a great and important success for detecting this disease from digital skin images via modifying/ updating some layers in the Transfer Learning. The other important information can be explained as because of being quite similar in some aspects to the other skin lesions and the lack of the detailed attributes/features of Monkeypox, detecting via specific AI models with Feature extraction process have become a bit difficult, unknown and time consuming in contrast to the Deep Learning models (AlexNet and VGG16 models in MATLAB software). The future aim is to develop a prototype web application and it is important that to improve the accuracy of Monkeypox detection, a larger demographically diverse dataset is required. |
| Anahtar Kelimeler |
| computer-aided diagnosis | deep learning | image processing | Monkeypox | skin lesion diagnosis |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | Traitement du Signal |
| Dergi ISSN | 0765-0019 |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q3 |
| Makale Dili | İngilizce |
| Basım Tarihi | 02-2023 |
| Cilt No | 40 |
| Sayı | 1 |
| Sayfalar | 383 / 388 |
| Doi Numarası | 10.18280/ts.400139 |
| Makale Linki | https://www.iieta.org/journals/ts/paper/10.18280/ts.400139 |
| Atıf Sayıları | |
| WoS | 1 |
| SCOPUS | 1 |
| Google Scholar | 4 |
