Classification of breast masses using Tactile Imaging System and machine learning algorithms

In this study, we used Tactile Imaging System (TIS) and machine learning algorithms to classify breast masses in vivo as malignant or benign. When the silicone probe at the front end of TIS is compressed against the breast mass, the indentation profile of this waveguide is captured by a CCD camera. Then TIS algorithm determines the size and stiffness of inclusions based on the acquired tactile images. The size and stiffness results are then used as the input features for breast tumor classification algorithms. We compared three tumor classification algorithms: k-nearest neighbor, support vector machine, and Naïve Bayes, which are known to work well for limited data set. We tested these algorithms on twelve human breast tumors. The results were evaluated using the leave-one-out cross validation technique. Among the three algorithms, k-nearest neighbor classifier performed the best with sensitivity of 86% and specificity of 100%.