TY  - JOUR
T1  - Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images
AU  - Karapınar Şentürk, Zehra
AU  - Güler, Recep
AU  - Gamsızkan, Mehmet
AU  - Özcan, Yunus
PY  - 2025
DA  - June
Y2  - 2025
DO  - 10.35377/saucis...1582098
JF  - Sakarya University Journal of Computer and Information Sciences
JO  - SAUCIS
PB  - Sakarya University
WT  - DergiPark
SN  - 2636-8129
SP  - 312
EP  - 321
VL  - 8
IS  - 2
LA  - en
AB  - Skin diseases are very common all over the world. The examination can be done by photographing the relevant area or taking a tissue sample to diagnose skin diseases. Examining tissue samples allows examination at the cellular level. This study discussed three skin diseases: lichen sclerosus, morphea, and cutaneous small vessel vasculitis (vasculitis). For this problem, which does not have an open-access dataset in the literature, a dataset consisting of histopathological images belonging to each class was created. Convolutional neural network models were created for this three-class classification problem, and their results were evaluated. In addition, in this problem where it is difficult to obtain sample images, the efficiency of transfer learning methods was evaluated with a limited number of examples. For this purpose, tests were performed with VGG16, ResNet50, InceptionV3, and EfficientNetB4 models, and the results were given. Among all the results, the accuracy value of the VGG16 model was 0.9755 and gave the best result. However, although the accuracy value was quite good, precision, recall, and f1-score metrics values were around 0.65. This shows deficiencies in how often the model correctly predicts the positive class and how well it predicts all positive examples in the dataset.
KW  - Convolutional neural networks
KW  - Data augmentation
KW  - Transfer learning
KW  - Histopathology
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UR  - https://doi.org/10.35377/saucis...1582098
L1  - https://dergipark.org.tr/en/download/article-file/4352010
ER  -