Preview

Medical Doctor and Information Technologies

Advanced search

Screening examination method for early differential diagnosis of skin neoplasms using mobile dermatoscopy

https://doi.org/10.25881/18110193_2025_3_50

Abstract

The aim of the study: development of a screening method for patients aimed at early differential diagnosis of malignant skin neoplasms using dermatoscopy in combination with optoelectronic mobile equipment and algorithms for classifying dermatoscopic images based on machine learning methods.
Materials and methods. To implement the detection of malignant neoplasms and classify them into the appropriate nosological group, machine learning methods, algorithms and optical recognition are used. The latter is used in the process of forming dermatoscopic images and training classification algorithms and models. The machine learning approaches are multi-class and binary cascade two-stage classification methods by classification algorithms based on the visual transformer architecture and neural network architecture.
Results. During the experimental evaluation of the results of multi-class classification (eight types of malignant neoplasms), the best classification model with the visual transformer architecture was determined, characterized by the metrics Accuracy of 0.932 and F-measure of 0.891 on the formed dataset, including ISIC-2019 and our own set containing 657 images. The binary cascade two-stage classification for melanocytic and non-melanocytic neoplasms has Accuracy and F-measure values — of 0.954 and 0.948 (the first stage of classification) and for melanomas and nevi — 0.964 and 0.951, respectively (the second stage of classification).
Conclusion. The obtained quantitative values of the malignant skin neoplasms detection accuracy by the developed screening examination method allow us to recommend the introduction of a multi-class classification for the primary division of a large volume of dermatoscopic images patients by nosological sign between medical specialists in the process of conducting mass (visiting) preventive examinations, and the introduction of a cascade binary classification in the an initial appointment conditions with limited access to specialized specialists to differentiate melanoma from other skin neoplasms. The developed screening examination method for patients can be introduced into medical practice as a system for supporting physician decision-making.

About the Authors

E. S. Kozachok
FSBIS ISP RAS
Russian Federation

Moscow



S. S. Seregin
BUZ Orlovskoi Oblasti OOD
Russian Federation

PhD

Oryol



A. V. Kozachok
FSBIS ISP RAS
Russian Federation

DSc, Associate Professor

Moscow



K. V. Eleckij
FSBIS ISP RAS
Russian Federation

PhD, Associate Professor

Moscow



O. I. Samovarov
FSBIS ISP RAS
Russian Federation

PhD

Moscow



References

1. Zlokachestvennye novoobrazovaniya v Rossii v 2023 godu (zabolevaemost' i smertnost'). Ed. by Kaprina A.D., Starinskogo V.V., Shahzadovoj A.O. Moscow: MNIOI im. P.A. Gercena − filial FGBU «NMIC radiologii» Minzdrava Rossii: 2024, 276 p. (In Russ.)

2. Krylovetskaya M.A., Komarov I.G., Karseladze D.A. Diagnosis and treatment of metastatic melanoma of unknown primary. Sovremennaya onkologiya. 2018; 20(3): 30-34. (In Russ.)] doi: 10.26442/1815-1434_2018.3.30-34.

3. Cancer Today. International Agency for Research on Cancer. 2025. Available at: https://gco.iarc.fr/today/en/dataviz/pie?mode=population&group_ populations=0. Accessed 11.07.2025.

4. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA: a cancer journal for clinicians. 2020; 70: 7-30. doi: 10.3322/caac.21590.

5. Bakheet S, Al-Hamadi A. Computer-Aided Diagnosis of Malignant Melanoma Using Gabor-Based Entropic Features and Multilevel Neural Networks. Diagnostics. 2020; 10(10): 822. doi: 10.3390/diagnostics10100822.

6. Pratiwi RA, Nurmaini S, Rini DP. Deep ensemble learning for skin lesions classification with convolutional neural network. IAES International Journal of Artificial Intelligence. 2021; 10(3): 563-570. doi: 10.11591/ijai.v10.i3.pp563-570.

7. Popescu D, El-Khatib M, El-Khatib H, Ichim L. New Trends in Melanoma Detection Using Neural Networks: A Systematic Review. Sensors. 2022; 22: 496. doi: 10.3390/s22020496.

8. El-Khatib M, Teodor OM, Popescu D, Ichim L. Identification of Benign Tumor Masses Using Deep Learning Techniques Based on Semantic Segmentation. Advances in Computational Intelligence (IWANN 2023). 2023; 14134: 529-540. doi: 10.1007/978-3-031-43085-5.

9. Hermosilla P, Soto R, Vega E, Suazo C, Ponce J. Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review. Diagnostics. 2024; 14: 454. doi: 10.3390/diagnostics14040454.

10. Mahmoud NM, Soliman AM. Early automated detection system for skin cancer diagnosis using artificial intelligent techniques. Scientific Reports. 2024; 14: 9749. doi: 10.1038/s41598-024-59783-0.

11. Kozachok AV, Spirin AA, Kozachok ES. Review of methods for early melanoma detection using computer vision methods. Trudy ISP RAN. 2022; 34(4): 241-250. (In Russ.)] doi: 10.15514/ISPRAS-2022-34(4)-17.

12. Khismatullina ZR, Chebotaryov VV, Babenko EA. Dermatoscopy in Dermato Oncology: Current Stateand Perspectives. Kreativnaya hirurgiya i onkologiya. 2024; 10(3): 241-248. (In Russ.)] doi: 10.24060/2076-3093-2020-10-3-241-248.

13. Kozachok AV, Spirin AA, Samovarov OI, Kozachok ES. Application of machine learning models for multiclass classification of dermatoscopic images of skin neoplasms. Trudy ISP RAN. 2024; 36(5) 241-252. (In Russ.)] doi: 10.15514/ISPRAS-2024-36(5)-17.

14. The AI community building the future. Available at: https://huggingface.co/models?pipeline_tag=imageclassification&sort=trending. Accessed 11.07.2025.

15. The International Skin Imaging Collaboration Available at: https:// www.isic-archive.com/. Accessed 11.07.2025.

16. Kamrul H, Asif A, Choon HY, Guang Y. A survey, review, and future trends of skin lesion segmentation and classification. Computers in Biology and Medicine. 2023; 155: 1-36. doi: 10.1016/j.compbiomed.2023. 106624.

17. Zhang Z, Lei Z, Omura M, Hasegawa H, Gao S. Dendritic Learning-Incorporated Vision Transformer for Image Recognition. Journal of Automatica Sinica. 2024; 11(2): 539-541. doi: 10.1109/JAS.2023.123978.

18. Han K, Wang Y, Chen H, Chen X, Guo J. A Survey on Vision Transformer. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022; 45(1): 87-110. doi: 10.1109/TPAMI.2022.3152247.

19. Bakulev AL, Konopatskova OM, Stanchina YV. Dermatoscopy in the diagnosis of pigmented nevi. Vestnik Dermatologii i Venerologii. 2019; 95(4): 48-56. (In Russ.)] doi: 10.25208/0042-4609-2019-95-4-48-56.

20. Codella N, Rotemberg V, Tschandl P, Celebi ME, et al. Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (ISIC). Computer Vision and Pattern Recognition. 2019. Available at: https://doi.org/10.48550/arXiv.1902.03368. Accessed 11.07.2025.

21. Almufareh MF, Tariq N, Humayun M, Khan FA. Melanoma identification and classification model based on finetuned convolutional neural network. Digital Heath. 2024; 10: 1-29. doi: 10.1177/20552076241253757.

22. Suleiman TA, Anyimadu1 DT, Permana AD, Ngim1 HA., Scotto di Freca A.. Two-step hierarchical binary classification of cancerous skin lesions using transfer learning and the random forest algorithm. Visual Computing for Industry, Biomedicine, and Art. 2024; 7(15): 1-17. doi: 10.1186/s42492-024-00166-7.


Review

For citations:


Kozachok E.S., Seregin S.S., Kozachok A.V., Eleckij K.V., Samovarov O.I. Screening examination method for early differential diagnosis of skin neoplasms using mobile dermatoscopy. Medical Doctor and Information Technologies. 2025;(3):50-63. (In Russ.) https://doi.org/10.25881/18110193_2025_3_50

Views: 4


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1811-0193 (Print)
ISSN 2413-5208 (Online)