## 5 Discussion
Moreover, a prototype imaging system should be developed and taken to clinical trials to determine which displacement estimator results in improved decision-making ability. Alternatively, data could be collected along with biopsy-confirmed results to attempt to create a diagnostic criterion for this new generation elastography system. Some notable non-inclusions in this study are normalized correlation coefficient methods and their variants, and deep-learning-based methods. NCC methods were not included in this investigation because the quality of the strain images; they produce low-quality displacement fields compared to the selected four estimators. The lack of sufficient quality associated with these methods is expected to impact reconstructed [PERSON]'s modulus images [45,46]. Moreover, NCC-based methods are not as computationally efficient as dynamic programming methods. Deep-learning-based methods were also not included in this investigation, as the performance of the network is highly dependent on the training data and the very specific architecture used [43, 44, 45, 46, 47], and deep learning methods should be evaluated on a case-by-case basis. Moreover, this investigation is primarily a CPU-based comparison; hence, including deep learning would lead to largely invalid conclusions about computation efficiency, as either they would be unfairly hampered by running on CPU or unfairly advantaged by running on GPU. The diagnosis of breast cancer is an inherently multi-faceted and non-binary process where clinicians must consider many different sources of evidence and come to a probability of malignancy or benign findings, which will inform the clinical course of action. Mathematical tools have been developed for this precise situation, including Dempster-Shafer Theory (DS Theory) and Fuzzy Logic, which are meant to provide a mathematical framework for integrating multiple sources of evidence and to deal with \"continuous\" logic, where probabilities are used rather than Boolean values. DS Theory is based upon Bayesian theories of subjective probability. It deals with mathematical constructs called \"belief functions,\" which integrate the answers to one question to influence the confidence or \"belief\" in a related question. In the domain of breast cancer diagnosis, it has been used to create new neural network methodologies that integrate multiple measures of breast lesion texture and pharmacokinetic parameters [48] to achieve superior results compared to traditional classifiers. Fuzzy logic was developed specifically to handle situations in which the truth value of some statement is not a Boolean value and is in fact a continuum between 0 and 1. In the case of breast cancer, it is not simply enough to assign it malignant or benign, as it cannot be known whether all benign lesions will remain that way or whether a malignant lesion will necessarily become life-threatening (overdiagnosis). Fuzzy logic was developed to tackle such an issue and has been used in diagnostic methods specifically to diagnose breast cancers [49]. The potential future direction of this work is to integrate the elastography technique identified as highly effective into such classification methodologies to improve the accuracy of breast cancer detection systems.