| Summary: | 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.
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