## 4 Discussion  
We compared the performance of the DE, SP\({}_{3}\), and ERT models with respect to three categories: feature extraction optimality, image classification performance, and computational efficiency. The first category is feature extraction optimality, where we compared the number of \"optimal\" features selected during the training process. In general, we prefer optimal feature vectors with low-dimensionality as this helps reduce the probability of over-fitting the data [36; 43]. Over-fitting can result in classification results that do not generalize well and therefore may be an unreliable predictor of future performance. In this work, the number of optimal features was eight for the DE model, three for the SP\({}_{3}\) model, and five for the ERT model. As we are generally interested in the fewest possible features to avoid over-fitting problems, it is clear that the SP\({}_{3}\) model is superior in this category to the ERT and DE models. The second category to compare was the classification performance of the three models. Here, we were primarily concerned with the sensitivity and the specificity that are computed by processing the data set reserved for testing with the classifier that results from the training phase. In addition to seeking values of the sensitivity and the specificity as close to 100.0% as possible, we were also interested in comparing the 95% confidence interval for each parameter. The CI is important because it informs us of the range within which we expect the true values of the sensitivity and the specificity to exist [35; 36; 44]. The reconstruction images computed with the SP\({}_{3}\) model allow for higher sensitivity and specificity values than the images obtained with the DE model. The DE model yielded a sensitivity of 67% at CI (47, 100)% and a specificity of 81% at CI (65, 100)%. The SP\({}_{3}\) model yielded a sensitivity of 88% at CI (78, 100)% and a specificity of 93% at CI (84.6, 100)%. Images computed with the ERT-based algorithm yielded a sensitivity of 91% at CI (83, 100)% and specificity of 98% at CI (85, 100)% specificity. Thus, the SP\({}_{3}\) model clearly outperformed the DE model and compares favorably to the ERT. We note that the upper bound of the CI for all models is 100%. The lower bound varies between the models and between the sensitivity and the specificity. As in the case of the sensitivity and the specificity values, the CI of the ERT was smaller than those obtained with the DE and SP\({}_{3}\) models. However, the lower bounds of the SP\({}_{3}\) model are significantly higher than the lower bounds of the DE model. The lower bound of the sensitivity with the DE model is in only 47%, which is significantly lower than the 78% lower bound that was computed with the SP\({}_{3}\) model. Furthermore, the sensitivity computed with the DE images (67%) is even lower than the lower bound of the sensitivity computed with the SP\({}_{3}\) model (78%). Similar results were observed when comparing the specificity values. The third category in which we compared the models was computational efficiency, which consists of the total reconstruction time and the total system resources needed. The reconstruction times with the DE and SP\({}_{3}\) models were similar, typically requiring less than 1 min to complete one reconstruction process on an Intel Core i9 processor. In contrast, the reconstruction time with the ERT model exceeded 100 min on the same computing platform [33]. Additionally, reconstructions with the ERT always required over 6 GB of RAM, while the DE and SP\({}_{3}\) models always required less than 200 MB of RAM [37; 38]. In the computational efficiency category, thus, the DE and SP\({}_{3}\) models outperform the ERT model. Overall, all these results discussed in this section indicate that the SP\({}_{3}\)-based reconstruction algorithm provides significant computational advantages over the ERT-based algorithm without compromising classification accuracy.