• Logo
  • HamaraJournals

Diagnosing Soft Tissue Sub-Surface Masses Using the XCS Classification System

Navid Moshtaghi Yazdani



Introduction: One of the most common types of cancer is breast cancer, which is considered as the second leading cause of death in women in Iran. Due to the fatality of this type of cancer, it is very important to diagnose the disease in the early stages and starting the treatment process. One of the methods to diagnose breast cancer is using mechanical arms (robot manipulator) to touch and measure the force in terms of displacement at the site of the breast touch by the robot. The hardness of the cancer tissue can affect the force diagram in terms of displacement, which can be used as a diagnostic method. The present study was performed to prepare a simulation model of breast soft tissue behavior considering subsurface masses. Then, a proposed classification system was designed to fit it.

Material and Methods: In this section, first, the soft tissue behavior of the breast is simulated by considering sub-surface masses. The simulations are performed for a piece of tissue that is in the shape of a rectangular cube, as well as different dimensions of a spherical mass that is located at different depths and coordinates. Using simulation, various force-displacement diagrams have been obtained, based on which a data network.

Results: The displacement force diagram for different modes is obtained using simulation. By giving the resulting diagrams to the trained system, the size and depth of the mass is determined. By comparing the obtained results with the initial model and the actual size and depth of the mass, a very good conformity is observed, which indicates the correct operation of the designed system and the performed simulation process.

Conclusion: The proposed design system was used to diagnose the presence of tumors in tissue with sub-surface mass. The results show a high percentage of this method in diagnosis. However, the accuracy of this method can be greatly increased by increasing the amount of data given to the XCS system for training. On the other hand, instead of simulation data, test data on healthy and unhealthy people can be used for training.


Laxminarayan R, Chow J, Shahid-Salles SA. Intervention cost-effectiveness: Overview of main messages. In: Jamison DT, Breman JG, Measham AR, Alleyne G, Claeson M, Evans DB, et al. (eds). Disease control priorities in developing countries. 2nd ed. Oxford University Press; 2006.

Mushlin AI, Kouides RW, Shapiro DE. Estimating the accuracy of screening mammography: A metaanalysis. Am J Prev Med. 1998; 14(2): 143-53. PMID: 9631167 DOI: 10.1016/s0749-3797(97)00019-6

Huber S, Wagner M, Medl M, Czembirek H. Realtime spatial compound imaging in breast ultra sound. Ultrasound Med Biol. 2002; 28(2): 155-63. PMID: 11937277 DOI: 10.1016/s0301-5629(01)00490-2

Harris JR, Lippman ME, Morrow M, Hellman S. Diseases of the breast. Lippincott Raven. 1996.

Ayyildiz M, Guclu B, Yildiz M, Basdogan C. A novel tactile sensor for detecting lumps in breast tissue. In: Kappers AML, van Erp JBF, Bergmann Tiest WM, van der Helm FCT (eds). Springer Berlin Heidelberg; 2010.

Roham H, Najarian S, Hosseini S, Dargahi J. Design and fabrication of a new tactile probe for measuring the modulus of elasticity of soft tissues. Sensor Review. 2007; 27(4): 317–23.

Sarvazyan AP, Skovoroda AR, Pyt'ev YP. Mechanical introscopy: A new modality of medical imaging for detection of breast and prostate cancer. Symposium on Computer Based Medical Systems. IEEE; 1997.

Mojra A, Najarian S, Towliat Kashani SM, Panahi F, Yaghmaei M. A novel haptic robotic viscogram for characterizing the viscoelastic behaviour of breast tissue in clinical examinations. Int J Med Robot. 2011; 7(3): 282-92. PMID: 21538774 DOI: 10.1002/rcs.396

Mojra A, Najarian S, Towliat Kashani SM, Panahi F, Tehrani MA. A novel robotic tactile mass detector with application in clinical breast examination. Minim Invasive Ther Allied Technol. 2012; 21(3): 210-21. PMID: 21919810 DOI: 10.3109/13645706.2011.602087

Mojra A, Najarian S, Towliat Kashani SM, Panahi F. A novel tactile-guided detection and threedimensional localization of clinically significant breast masses. J Med Eng Technol. 2012; 36(1): 8-16. PMID: 22074118 DOI: 10.3109/03091902.2011.629275

Sarvazyan A, Egorov V, JS Son, Kaufman C. Cost effective screening for breast cancer worldwide: Current state and future directions. Breast Cancer (Auckl). 2008; 1: 91-9. PMID: 19578481 DOI: 10.4137/bcbcr.s774

Shariat Panahi M, Moshtaghi Yazdani N. An improved XCSR classifier system for data mining with limited trainingSamples. Global Journal of Science, Engineering and Technology. 2012; 2; 52-7.

DOI: http://dx.doi.org/10.30699/fhi.v9i1.239


  • There are currently no refbacks.