Research Article
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Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images

Year 2025, Volume: 8 Issue: 2, 312 - 321, 30.06.2025

Recep Güler , Zehra Karapınar Şentürk , Mehmet Gamsızkan , Yunus Özcan

https://doi.org/10.35377/saucis...1582098

Abstract

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.

Keywords

Convolutional neural networks Data augmentation Transfer learning Histopathology

Ethical Statement

This study does not require permission from the ethics committee or any special permission. The microscopic skin images used do not contain any personal data.

Thanks

The authors would like to thank Research Assistant Dr. Sümeyye Güneş Takır for creating the dataset used in this study.

References

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Year 2025, Volume: 8 Issue: 2, 312 - 321, 30.06.2025

Recep Güler , Zehra Karapınar Şentürk , Mehmet Gamsızkan , Yunus Özcan

https://doi.org/10.35377/saucis...1582098

Abstract

References

  • R. Pangti et al., “A machine learning-based, decision support, mobile phone application for diagnosis of common dermatological diseases,” Journal of the European Academy of Dermatology and Venereology, vol. 35, no. 2, pp. 536–545, Feb. 2021, doi: 10.1111/JDV.16967.
  • O. Jimenez-Del-Toro et al., “Analysis of Histopathology Images: From Traditional Machine Learning to Deep Learning,” Biomedical Texture Analysis: Fundamentals, Tools and Challenges, pp. 281–314, Jan. 2017, doi: 10.1016/B978-0-12-812133-7.00010-7.
  • G. Litjens et al., “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci Rep, vol. 6, no. 1, p. 26286, May 2016, doi: 10.1038/srep26286.
  • C. Y. Zhu et al., “A Deep Learning Based Framework for Diagnosing Multiple Skin Diseases in a Clinical Environment,” Front Med (Lausanne), vol. 8, p. 626369, Apr. 2021, doi: 10.3389/FMED.2021.626369/BIBTEX.
  • M. Tanaka et al., “Classification of a large-scale image database of various skin diseases using deep learning,” Int J Comput Assist Radiol Surg, vol. 16, no. 11, pp. 1875–1887, Nov. 2021, doi: 10.1007/S11548-021-02440-Y/FIGURES/8.
  • R. Sadik, A. Majumder, A. A. Biswas, B. Ahammad, and M. M. Rahman, “An in-depth analysis of Convolutional Neural Network architectures with transfer learning for skin disease diagnosis,” Healthcare Analytics, vol. 3, p. 100143, Nov. 2023, doi: 10.1016/J.HEALTH.2023.100143.
  • E. Goceri, “Deep learning based classification of facial dermatological disorders,” Comput Biol Med, vol. 128, p. 104118, Jan. 2021, doi: 10.1016/J.COMPBIOMED.2020.104118.
  • L. Duran-Lopez, J. P. Dominguez-Morales, A. F. Conde-Martin, S. Vicente-Diaz, and A. Linares-Barranco, “PROMETEO: A CNN-Based Computer-Aided Diagnosis System for WSI Prostate Cancer Detection,” IEEE Access, vol. 8, pp. 128613–128628, 2020, doi: 10.1109/ACCESS.2020.3008868.
  • Y. Wu et al., “Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications,” Cancers (Basel), vol. 14, no. 5, p. 1199, Feb. 2022, doi: 10.3390/cancers14051199.
  • A. Mumuni and F. Mumuni, “Data augmentation: A comprehensive survey of modern approaches,” Array, vol. 16, p. 100258, Dec. 2022, doi: 10.1016/J.ARRAY.2022.100258.
  • F. Perez, C. Vasconcelos, S. Avila, and E. Valle, “Data augmentation for skin lesion analysis,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11041 LNCS, pp. 303–311, 2018, doi: 10.1007/978-3-030-01201-4_33/FIGURES/3.
  • A. Mikołajczyk and M. Grochowski, “Data augmentation for improving deep learning in image classification problem,” 2018 International Interdisciplinary PhD Workshop, IIPhDW 2018, pp. 117–122, Jun. 2018, doi: 10.1109/IIPHDW.2018.8388338.
  • K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/J.GLTP.2022.04.020.
  • C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J Big Data, vol. 6, no. 1, pp. 1–48, Dec. 2019, doi: 10.1186/S40537-019-0197-0/FIGURES/33.
  • M. S. Ali, M. S. Miah, J. Haque, M. M. Rahman, and M. K. Islam, “An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models,” Machine Learning with Applications, vol. 5, p. 100036, Sep. 2021, doi: 10.1016/J.MLWA.2021.100036.
  • A. Saber, M. Sakr, O. M. Abo-Seida, A. Keshk, and H. Chen, “A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique,” IEEE Access, vol. 9, pp. 71194–71209, 2021, doi: 10.1109/ACCESS.2021.3079204.
  • M. Fraiwan and E. Faouri, “On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning,” Sensors 2022, Vol. 22, Page 4963, vol. 22, no. 13, p. 4963, Jun. 2022, doi: 10.3390/S22134963.
  • H. Aljuaid, N. Alturki, N. Alsubaie, L. Cavallaro, and A. Liotta, “Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning,” Comput Methods Programs Biomed, vol. 223, p. 106951, Aug. 2022, doi: 10.1016/J.CMPB.2022.106951.
  • S. Hosseinzadeh Kassani, P. Hosseinzadeh Kassani, M. J. Wesolowski, K. A. Schneider, and R. Deters, “Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models,” Int J Med Inform, vol. 159, p. 104669, Mar. 2022, doi: 10.1016/J.IJMEDINF.2021.104669.
  • N. Ahmad, S. Asghar, and S. A. Gillani, “Transfer learning-assisted multi-resolution breast cancer histopathological images classification,” Visual Computer, vol. 38, no. 8, pp. 2751–2770, Aug. 2022, doi: 10.1007/S00371-021-02153-Y/FIGURES/21.
  • A. Ben Hamida et al., “Deep learning for colon cancer histopathological images analysis,” Comput Biol Med, vol. 136, p. 104730, Sep. 2021, doi: 10.1016/J.COMPBIOMED.2021.104730.
  • P. Kora et al., “Transfer learning techniques for medical image analysis: A review,” Biocybern Biomed Eng, vol. 42, no. 1, pp. 79–107, Jan. 2022, doi: 10.1016/J.BBE.2021.11.004.
  • M. Iman, H. R. Arabnia, and K. Rasheed, “A Review of Deep Transfer Learning and Recent Advancements,” Technologies 2023, Vol. 11, Page 40, vol. 11, no. 2, p. 40, Mar. 2023, doi: 10.3390/TECHNOLOGIES11020040.
  • H. E. Kim, A. Cosa-Linan, N. Santhanam, M. Jannesari, M. E. Maros, and T. Ganslandt, “Transfer learning for medical image classification: a literature review,” BMC Medical Imaging 2022 22:1, vol. 22, no. 1, pp. 1–13, Apr. 2022, doi: 10.1186/S12880-022-00793-7.
  • A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems, F. Pereira, C. J. Burges, L. Bottou, and K. Q. Weinberger, Eds., Curran Associates, Inc., 2012.
  • K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, Sep. 2014, Accessed: Sep. 06, 2024.
  • C. Szegedy et al., “Going Deeper with Convolutions,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 07-12-June-2015, pp. 1–9, Sep. 2014, doi: 10.1109/CVPR.2015.7298594.
  • K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-December, pp. 770–778, Dec. 2015, doi: 10.1109/CVPR.2016.90.
  • “ImageNet,” https://www.image-net.org/.
There are 29 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Article
Authors

Recep Güler 0009-0004-9715-9006

Zehra Karapınar Şentürk 0000-0003-3116-1985

Mehmet Gamsızkan 0000-0001-9942-4898

Yunus Özcan 0000-0002-2295-1152

Early Pub Date June 20, 2025
Publication Date June 30, 2025
Submission Date November 10, 2024
Acceptance Date April 12, 2025
Published in Issue Year 2025Volume: 8 Issue: 2

Cite

APA Güler, R., Karapınar Şentürk, Z., Gamsızkan, M., Özcan, Y. (2025). Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images. Sakarya University Journal of Computer and Information Sciences, 8(2), 312-321. https://doi.org/10.35377/saucis...1582098
AMA Güler R, Karapınar Şentürk Z, Gamsızkan M, Özcan Y. Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images. SAUCIS. June 2025;8(2):312-321. doi:10.35377/saucis.1582098
Chicago Güler, Recep, Zehra Karapınar Şentürk, Mehmet Gamsızkan, and Yunus Özcan. “Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images”. Sakarya University Journal of Computer and Information Sciences 8, no. 2 (June 2025): 312-21. https://doi.org/10.35377/saucis. 1582098.
EndNote Güler R, Karapınar Şentürk Z, Gamsızkan M, Özcan Y (June 1, 2025) Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images. Sakarya University Journal of Computer and Information Sciences 8 2 312–321.
IEEE R. Güler, Z. Karapınar Şentürk, M. Gamsızkan, and Y. Özcan, “Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images”, SAUCIS, vol. 8, no. 2, pp. 312–321, 2025, doi: 10.35377/saucis...1582098.
ISNAD Güler, Recep et al. “Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 2025), 312-321. https://doi.org/10.35377/saucis. 1582098.
JAMA Güler R, Karapınar Şentürk Z, Gamsızkan M, Özcan Y. Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images. SAUCIS. 2025;8:312–321.
MLA Güler, Recep et al. “Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, 2025, pp. 312-21, doi:10.35377/saucis. 1582098.
Vancouver Güler R, Karapınar Şentürk Z, Gamsızkan M, Özcan Y. Diagnosis of Lichen Sclerosus, Morphea, and Vasculitis Using Deep Learning Techniques on Histopathological Skin Images. SAUCIS. 2025;8(2):312-21.
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