PROPOSAL FOR A VIBRATION-BASED SYSTEM FOR THE PRELIMINARY DIAGNOSIS OF NONINVASIVE SKIN TUMORS

Öz

Early diagnosis of skin tumors is one of the most critical factors affecting treatment success and survival rates. Early diagnosis of aggressive skin cancers such as malignant melanoma is of vital importance and can significantly increase patient survival rates. Currently widely used diagnostic methods such as dermoscopic examination, image processing-based analyses, and biopsy are based on subjective interpretations and require invasive interventions, leading to both time loss and reduced patient comfort. In this study, a non-invasive and portable pre-diagnostic system based on biomechanical differences between healthy and tumorous skin tissues, operating with low-frequency vibration stimulation, is proposed. The proposed system consists of an actuator that provides vibration stimulation at a fixed frequency, a three-axis accelerometer (ADXL355), and Raspberry Pi 5 hardware. The system detects mechanical changes within the tissue by analyzing the responses measured at different distances from specific points on the tissue to which mechanical vibrations are applied. The developed system has been tested on both physical and computer modeled skin phantoms. Significant differences in dominant frequency and damping characteristics were observed between tumorous and healthy tissues in the signals obtained in response to a 120-180-200 Hz. vibration stimulus. Less damp and higher frequency responses were recorded in tumorous regions. This indicates that increased tissue stiffness affects vibration characteristics. The results suggest that the proposed system is a low-cost, patient-friendly, and usable supportive system for early diagnosis. Future studies aim to test the system on real tissues and integrate it into clinical applications.

Anahtar Kelimeler

• Skin Tumor
• Noninvasive Diagnosis
• Vibration Signal
• FFT
• Numerical Modeling.

NONİNVAZİV CİLT TÜMÖRLERİNİN ÖN TANISI İÇİN TİTREŞİM TABANLI SİSTEM ÖNERİSİ

Öz

Cilt tümörlerinin erken evrede teşhisi, tedavi başarısını ve sağkalım oranını etkileyen en kritik faktörlerdendir. Özellikle malign melanom gibi agresif cilt kanserlerinin erken evrelerde teşhisi hayati önem taşımakta olup, hasta sağkalım oranlarını anlamlı ölçüde artırabilmektedir. Günümüzde yaygın olarak kullanılan dermoskopik muayene, görüntü işleme tabanlı analizler ve biyopsi gibi tanı yöntemleri subjektif yorumlara dayalı olmakla birlikte, invaziv müdahaleler gerektirmekte ve zaman kaybının yanı sıra hasta konforunun azalmasına yol açmaktadır. Bu çalışmada, sağlıklı ve tümörlü cilt dokuları arasındaki biyomekanik farklılıklara dayanan, düşük frekanslı titreşim uyartımıyla çalışan, noninvaziv ve taşınabilir bir ön tanı sistemi önerilmektedir. Önerilen sistem; sabit frekansta titreşim uyartımı veren bir aktüatör, üç eksenli ivmeölçer (ADXL355) ve Raspberry Pi 5 donanımından oluşmaktadır. Sistem, dokular üzerindeki belirli noktalara uygulanan mekanik titreşimlerin belirli uzaklıklarda ölçülen yanıtlarını analiz ederek doku içindeki mekanik değişiklikleri tespit etmektedir. Geliştirilen sistem hem fiziksel hem de bilgisayar ortamında modellenen cilt fantomları üzerinde edilmiştir. 120-180-200 Hz.’lik titreşim uyartımı verilen tümörlü ve sağlıklı dokular arasında baskın frekans ve sönümleme özelliklerinde belirgin farklılıklar gözlemlenmiştir. Özellikle tümörlü bölgelerde daha az sönüm ve daha yüksek frekanslı yanıtlar kaydedilmiştir. Bu durum, doku içi sertlik artışının titreşim karakteristiklerini etkilediğini göstermektedir. Sonuçlar, önerilen sistemin düşük maliyetli, hasta dostu ve erken tanıya yönelik kullanılabilir destekleyici bir sistem olduğunu göstermektedir. Gelecek çalışmalarda sistemin gerçek dokular üzerinde test edilmesi ve klinik uygulamalara entegrasyonu hedeflenmektedir.

Anahtar Kelimeler

• Cilt Tümörü
• Noninvaziv Tanı
• Titreşim Sinyali
• FFT
• Sayısal Modelleme

Kaynakça

1. 1. Apalla, Z., Nashan, D., Weller, R.B. and Castellsagué, X. “Skin cancer: epidemiology, disease burden, pathophysiology, diagnosis, and therapeutic approaches”, Dermatology and Therapy, Vol. 7, Pages 5-19, 2017.
2. 2. Garbe, C., and Leiter, U. “Melanoma epidemiology and trends”, Clinics in Dermatology, Vol. 27, Issue 1, Pages 3-9, 2009.
3. 3. Balch, C.M., Soong, S.J., Gershenwald, J.E., Thompson, J.F., Reintgen, D.S., Cascinelli, N., Urist, M., McMasters, K.M., Ross, M., Kirkwood, J.M., Atkins, M.B., Thompson, J.A., Coit, D.G., Byrd, D., Desmond, R., Zhang, Y., Liu, P.Y., Lyman, G.H. and Morabito, A. “Prognostic factors analysis of 17,600 melanoma patients: validation of the American Joint Committee on Cancer melanoma staging system”, Journal of Clinical Oncology, Vol. 19, Issue 16, Pages 3622-3634, 2001.
4. 4. Garbe, C. and Peris, K. “European consensus-based interdisciplinary guideline for melanoma. Part 2: Treatment – Update 2019”, European Journal of Cancer, Vol. 126, Pages 159-177, 2020.
5. 5. Karatepe F., Tas B., Coşkun Ö. and Kahriman M., “Detection of Escherichia Coli Bacteria by Using Image Processing Techniques”, IARAS International Journal of Biology and Biomedical Engineering, 245-251, 2022.
6. 6. Kittler, H., Pehamberger, H., Wolff, K. and Binder, M. “Diagnostic accuracy of dermoscopy”, The Lancet Oncology, Vol. 3, Issue 3, Pages 159-165, 2010.
7. 7. Hobayan, C. G. P., Gray, A. N., Waters, M. F., Mager, L. A., Kobayashi, S., Essien, E. W., Ulman, A.C and Kaffenberger, B. H. “Diagnostic accuracy of high-frequency ultrasound for cutaneous neoplasms: a narrative review of the literature”, Archives of Dermatological Research, Vol. 316, Issue 7, Pages 419, 2024.
8. 8. Plodinec, M., Loparic, M., Monnier, C.A., Obermann, E.C., Zanetti-Dallenbach, R., Oertle, P., Hyotyla, J. T., Aebi, U., Bentires-Alj, M., Lim, R. Y. H. and Schoenenberger, C. A. “The nanomechanical signature of breast cancer”, Nature nanotechnology, Vol. 7 Issue 11, Pages 757-765, 2012.

9. 9. Samani A., Zubovits, J. and Plewes, D. “Elastic Moduli of Normal and Pathological Human Breast Tissues: An Inversion-Technique-Based Investigation of 169 Samples”, Physics in Medicine and Biology, Vol. 52, Issue 6, Pages 1565, 2007.
10. 10. Lekka, M. “Discrimination between normal and cancerous cells using AFM”, Bionanoscience, Vol. 6, Issue 1, Pages 65-80, 2016.
11. 11. Liang, X. and Boppart, S. A. “Biomechanical properties of in vivo human skin from dynamic optical coherence elastography”, IEEE Transactions on Biomedical Engineering, Vol. 57, Issue 4, Pages 953-959, 2009.
12. 12. Ak, M. U., Bilgin, S., Oral, O., Carlak, H. F., Derin, A. T. and Derin, N. “Evaluation of Vibration Measurements on The Human Face Using Median and Maximum Frequencies”, IET Science, Measurement & Technology, Vol. 14, Issue 8, Pages 853-856, 2020.
13. 13. Mariappan, Y. K., Glaser, K. J. and Ehman, R. L. “Magnetic resonance elastography: A review”, Clinical Anatomy, Vol. 23, Issue 5, Pages 497-511, 2020.
14. 14. Sarvazyan, A. P., Rudenko, O. V., Swanson, S. D., Fowlkes, J. B. and Emelianov, S. Y. “Shear wave elasticity imaging: A new ultrasonic technology of medical diagnostics”, Ultrasound in Medicine & Biology, Vol. 24, Issue 9, Pages 1419-1435, 1998.
15. 15. Lee, H. G. and Moheimani, S. O. R. “A high sensitivity MEMS accelerometer for vibration sensing”, IEEE Sensors Journal, Vol. 12, Issue 8, Pages 2419-2425, 2012.
16. 16. Baghdadi, H., Rhofir, K., & Lamhamdi, M. “Smart portable system for monitoring vibration based on the Raspberry Pi microcomputer and the MEMS accelerometer”, International Journal of Informatics and Communication Technology, Vol. 12, Issue 3, Pages 261-271, 2023
17. 17. Madsen, E. L., Zagzebski, J. A., Banjavic, R. A., and Jutila, R. E. “Tissue mimicking materials for ultrasound phantoms”, Medical Physics, Vol. 5, Issue 5, Pages 391-394, 1978.
18. 18. Vanmunster, M., Rojo-Garcia, A. V., Pacolet, A., Jonkers, I., Koppo, K., Lories, R., and Suhr, F. Prolonged mechanical muscle loading increases mechanosensor gene and protein levels and causes a moderate fast-to-slow fiber type switch in mice. Journal of Applied Physiology, Vol. 135, Issue 4, Pages 918-931, 2023.
19. 19. Nightingale, K. “Acoustic radiation force impulse (ARFI) imaging: a review”, Current medical imaging reviews, Vol. 7, Issue 4, Pages 328-339, 2011.
20. 20. Özkaya U., Coşkun Ö. and Çömlekçi S., “Frequency Analysis of EMG Signals with Matlab Sptool”, Proceedings of the 9th WSEAS International Conference on Signal Processing, 2010.
21. 21. Dong, Y., Lu, M., Yin, Y., Wang, C. and Dai, N. “Tumor Biomechanics-Inspired Future Medicine”, Cancers, Vol. 16, Issue 23, Pages 4107, 2024.
22. 22. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Jeroen A.W.M., Laak, V. D., Ginneken, B. V. and Sánchez, C. I. “A survey on deep learning in medical image analysis. Medical image analysis, Vol. 42, Issue 1, Pages 60-88, 2017.
23. 23. Hosny, K. M., Kassem, M. A. and Foaud, M. M. “Skin cancer classification using deep learning and transfer learning”. IEEE 9th Cairo international biomedical engineering conference (CIBEC), Pages 90-93, 2018.
24. 24. Chen, A. I., Balter, M. L., Chen, M. I., Gross, D., Alam, S. K., Maguire, T. J. and Yarmush, M. L., “Multilayered tissue mimicking skin and vessel phantoms with tunable mechanical, optical, and acoustic properties”, Medical physics, Vol. 43, İssue 6(Kısım 1), Pages 3117-3131, 2016.
25. 25. Zhou, L., Aljiffry, A., Lee, Y. J., Matthews, J., Seitter, B., Soltis, I., Huang, Y., Maher, K. and Yeo, W. H. “Soft imperceptible wearable electronics for at-home cardiovascular monitoring of infants with single ventricle heart disease”, Biosensors and Bioelectronics, Vol 278, 117372, 2025.
26. 26. Analog Devices. “ADXL354/ADXL355”, https://www.analog.com/media/en/technicaldocumentation/datasheets/adxl354_adxl355.pdf/, Temmuz 01, 2025.
27. 27. Baraneedharan, P., Kalaivani, S., Vaishnavi, S. and Somasundaram, K. “Revolutionizing healthcare: A review on cutting-edge innovations in Raspberry Pi-powered health monitoring sensors”, Computers in Biology and Medicine, Vol. 190, 110109, 2025.
28. 28. Precision Microdrives. “Vibration Motor Datasheet”, htps://www.precisionmicrodrives.com/product/datasheet/310-103-10mm-vibration-motor-3mm-typedatasheet.pdf, Ekim 15, 2025.
29. 29. Coyte, J.L., Stirling, D., Du, H. and Ros, M. “Seated Whole-Body Vibration Analysis, Technologies, and Modeling: A Survey”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 46, Issue 6, Pages 725-739, 2015. 02
30. 30. Shannon, C.E. “Communication in The Presence of Noise”, Proceedings of the IRE, Vol. 37, Issue 1, Pages 10-21, 1949.
31. 31. Kabir, N. A., Okoh, F. O. and Yusof, M. F. M. “Radiological and physical properties of tissue equivalent mammography phantom: Characterization and analysis methods”, Radiation Physics and Chemistry, Vol. 180, 109271, 2021.
32. 32. Yeh, J. Y. “Vibration characteristics analysis of orthotropic rectangular sandwich plate with magnetorheological elastomer”, Procedia Engineering, Vol. 79, Issue 1, Pages 378-385, 2014