| Yazarlar |
M. Roshni Thanka
Türkiye |
|
E. Bijolin Edwin
Türkiye |
|
V. Ebenezer
Türkiye |
|
K. Martin Sagayam
Türkiye |
|
B. Jayakeshav Reddy
Türkiye |
Hatıra GÜNERHAN
Kafkas Üniversitesi, Türkiye |
|
Homan Emadifar
Türkiye |
| Özet |
| Generally, Melanoma, Merkel cell cancer, Squamous cell carcinoma, and Basal cell carcinoma, are the four major categories of skin cancers. In contrast to other cancer types, melanoma, a type of skin cancer, affects a lot of people. Early identification and prediction of this skin cancer can avoid the risk of spreading to another part of the body which can be treated and cured effectively. The advancing machine learning and deep learning approaches create an efficient computerized diagnosis system that can assist physicians to predict the disease in a much faster way, and enable the affected person to identify it skillfully. The existing models either rely on machine learning models which are limited to feature selection or deep learning-based methods that learn features from full images. The proposed hybrid pre-trained convolutional neural network and machine learning classifiers are used for feature extraction and classification. This kind of approach improves the model's accuracy. Here the hybrid VGG16 and XGBoost is used as feature extraction and as a classifier, this integration obtains maximum accuracy of 99.1%, which is higher accuracy compared to other works represented in the literature survey. |
| Anahtar Kelimeler |
| Benign | Classifier | Deep learning | Feature extractor | Machine learning | Melanoma | Skin cancers |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SCOPUS dergilerinde yayımlanan tam makale |
| Dergi Adı | Computer Methods and Programs in Biomedicine Update |
| Dergi ISSN | 2666-9900 |
| Dergi Tarandığı Indeksler | Scopus |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2023 |
| Cilt No | 3 |
| Sayı | 1 |
| Sayfalar | 100103 / 0 |
| Doi Numarası | 10.1016/j.cmpbup.2023.100103 |
| Makale Linki | http://dx.doi.org/10.1016/j.cmpbup.2023.100103 |
| Atıf Sayıları | |
| SCOPUS | 25 |
| Google Scholar | 37 |
