TY  - JOUR
TI  - DIAGNOSING DISEASES FROM FINGERNAIL IMAGES
AB  - This paper investigates how people's finger and nail appearance helps diagnose various diseases, such as Darier's disease, Muehrcke's lines, alopecia areata, beau's lines, bluish nails, and clubbing, by image processing and deep learning techniques. We used a public dataset consisting of 17 different classes with 655 samples. We divided the dataset into three folds based on a widely used rule, the 0.7:0.2:0.1, for training, validation, and testing purposes. We tested the EfficientNet-B2 model for performance evaluation purposes by using Noisy-Student weights by setting the batch size and epochs as 32 and 1000. The model achieves a 72% accuracy score and 91% AUC score for test samples to detect fingernail diseases. The empirical findings in this study provide a new understanding that the EfficientNet-B2 model can categorize nail disease types through numerous classes.
AU  - Can, Zuhal
AU  - isik, sahin
DO  - 10.31796/ogummf.1111749
PY  - 2022
JO  - Eskişehir Osmangazi Üniversitesi mühendislik ve mimarlık fakültesi dergisi (online)
VL  - 30
IS  - 3
SN  - 2630-5712
SP  - 464
EP  - 470
DB  - TRDizin
UR  - http://search/yayin/detay/1148755
ER  -