Conceptualization and Feasibility Testing of a Vibro-Acoustic Solution for Tumor Detection

Accurate detection of tumors is critical for the success of oncologic surgical intervention. Along with any modality for the localization of tumors, surgeons achieve important information with direct palpation as long as direct touch of the tumor is possible. However, often due to using surgical tools in confined cavities or employing robotic endeffectors, direct palpation is not possible. We propose a low-frequency vibro-acoustic vibration sensing method to identify tumors based on their mechanical properties. The tumor detection problem was cast into a binary classification as a complementary modality. The method involves a wavelet-based multilayer perceptron neural network that is trained in a supervised manner. Some phantoms of healthy tissue with tumor model inclusion were used for experiments and data collection. From the 120 overall number of experiments, 18 data (15% of the whole data samples) were used as test data to evaluate the performance. The results show an 83.3% accuracy related to the confusion matrix, reflecting the model performance on previously unseen data. Performance of the classifier was evaluated using the confusion matrix and the receiver operating characteristic (ROC). It is discussed that the advantage of the proposed system is that the sensor is not directly touching the specimen and can be integrated into the probe end due to its small dimension.