Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images Article Swipe
Recep Güler
,
Zehra Karapınar Şentürk
,
Mehmet Gamsızkan
,
Yunus Özcan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.35377/saucis...1582098
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.35377/saucis...1582098
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.35377/saucis...1582098
- http://saucis.sakarya.edu.tr/en/download/article-file/4352010
- OA Status
- diamond
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411499108
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411499108Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.35377/saucis...1582098Digital Object Identifier
- Title
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Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin ImagesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-20Full publication date if available
- Authors
-
Recep Güler, Zehra Karapınar Şentürk, Mehmet Gamsızkan, Yunus ÖzcanList of authors in order
- Landing page
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https://doi.org/10.35377/saucis...1582098Publisher landing page
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https://saucis.sakarya.edu.tr/en/download/article-file/4352010Direct link to full text PDF
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YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
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https://saucis.sakarya.edu.tr/en/download/article-file/4352010Direct OA link when available
- Concepts
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Lichen sclerosus, Convolutional neural network, Histopathological examination, Artificial intelligence, Dermatology, Vasculitis, Medicine, Morphea, Class (philosophy), Pathology, Computer science, DiseaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W3090260921, https://openalex.org/W2751274270, https://openalex.org/W2401520370, https://openalex.org/W3155556844, https://openalex.org/W3186907113, https://openalex.org/W4318777809, https://openalex.org/W3104427612, https://openalex.org/W3042405885, https://openalex.org/W4214547251, https://openalex.org/W4309028162, https://openalex.org/W2890655382, https://openalex.org/W2809598685, https://openalex.org/W4226323522, https://openalex.org/W2954996726, https://openalex.org/W3159690776, https://openalex.org/W3163936961, https://openalex.org/W4283757235, https://openalex.org/W4282982312, https://openalex.org/W4205428558, https://openalex.org/W3163306278, https://openalex.org/W3191219235, https://openalex.org/W4200453770, https://openalex.org/W4324142429, https://openalex.org/W4223899585, https://openalex.org/W2163605009, https://openalex.org/W1686810756, https://openalex.org/W2097117768, https://openalex.org/W2194775991 |
| referenced_works_count | 28 |
| abstract_inverted_index.a | 21, 65, 115 |
| abstract_inverted_index.In | 93 |
| abstract_inverted_index.an | 59 |
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| abstract_inverted_index.The | 9 |
| abstract_inverted_index.all | 5, 139, 191 |
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| abstract_inverted_index.with | 114, 126 |
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| abstract_inverted_index.given. | 137 |
| abstract_inverted_index.images | 70 |
| abstract_inverted_index.level. | 36 |
| abstract_inverted_index.lichen | 43 |
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| abstract_inverted_index.neural | 78 |
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| abstract_inverted_index.sample | 23, 104 |
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| abstract_inverted_index.results | 90, 135 |
| abstract_inverted_index.samples | 30 |
| abstract_inverted_index.However, | 156 |
| abstract_inverted_index.accuracy | 143, 159 |
| abstract_inverted_index.although | 157 |
| abstract_inverted_index.cellular | 35 |
| abstract_inverted_index.created. | 76 |
| abstract_inverted_index.dataset. | 196 |
| abstract_inverted_index.diagnose | 25 |
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| abstract_inverted_index.f1-score | 167 |
| abstract_inverted_index.learning | 110 |
| abstract_inverted_index.morphea, | 45 |
| abstract_inverted_index.positive | 184, 192 |
| abstract_inverted_index.predicts | 182, 190 |
| abstract_inverted_index.problem, | 54, 87 |
| abstract_inverted_index.purpose, | 122 |
| abstract_inverted_index.relevant | 17 |
| abstract_inverted_index.results, | 141 |
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| abstract_inverted_index.Examining | 28 |
| abstract_inverted_index.ResNet50, | 128 |
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| abstract_inverted_index.open-access | 60 |
| abstract_inverted_index.three-class | 85 |
| abstract_inverted_index.InceptionV3, | 129 |
| abstract_inverted_index.deficiencies | 175 |
| abstract_inverted_index.(vasculitis). | 51 |
| abstract_inverted_index.Convolutional | 77 |
| abstract_inverted_index.photographing | 15 |
| abstract_inverted_index.EfficientNetB4 | 131 |
| abstract_inverted_index.classification | 86 |
| abstract_inverted_index.histopathological | 69 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.37546787 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |