## 3 Results and Discussion  
The ensemble learning approach was evaluated on five datasets of H&E histological images, as described in Section 2, with comparisons involving the different classes of each set. It is important to note that 45 types of tests were performed to explore different compositions of ensembles, including three associations of wrapper methods, in order to provide the main compositions among the 100 best-ranked descriptors with ReliefF. Each composition was evaluated via a heterogeneous ensemble of classifiers (Section 2.6.1). In Table 5, the average performances for each ensemble, considering the HFs and DL attributes, are shown. The best rates are highlighted in bold. From Table 5, it is observed that the HFs and HFs+DL ensembles were responsible for the best results in four H&E datasets (OED, LA, LG, and NHL) out of the five investigated here. The accuracy values ranged from 90.72% to 100%. Thus, it is possible  
Figure 4: Examples of NHL [101] **(a)**, OED [100] **(b)**, CR [99] **(c)**, LG **(d)** and LA **(e)** images [102]. to indicate that the handcrafted descriptors explored here (via HFs ensemble) are relevant for the classification process whether used separately or in combination with DL. The HFs ensemble provided the highest distinction rate on the OED dataset regardless of the wrapper selector taken as reference, indicating the optimal match on this dataset, which represents a further contribution of the proposed approach. On the other hand, HFs presented the lowest accuracy values (approximately 78%) in the NHL dataset, with three classes (CLL\(\times\)FL\(\times\)MCL), demonstrating a possible limit of HFs. When this category was combined with DL (HFs\(+\)DL ensemble), the result was the most expressive for the NHL dataset with an accuracy of 90.72%. Even so, this result represents an accuracy of at least 8% lower than the values achieved on other datasets. This is an important indication of the difficulties in distinguishing CLL\(\times\)FL\(\times\)MCL groups, especially considering only the HFs ensemble. Finally, when the DL ensemble is considered, the best solution was achieved in a single dataset (CR) but with an expressive rate (99.76% accuracy), illustrating its importance for the development of strategies to support the diagnosis of colorectal cancer. In addition, on this dataset, it is worth mentioning the HFs+DL ensemble as another potential solution, which achieved an accuracy of 99.58%, which was very close to that provided by the DL combination. This configuration represents an acceptable and common solution for different types of histological samples. To summarize the results discussed here, the best combinations of descriptors and selection algorithms are presented in Table 6, including the total number of descriptors, AUC, and accuracy averages. The accuracy values of the top 1 and top 10 solutions are also indicated, making it possible to observe the existing variation for the first and tenth solutions in each dataset, since the averages were calculated from the 10 best-ranked compositions in each dataset. It is important to emphasize that this ranking indicated the highest accuracy with the lowest number of descriptors. Based on the previously stated criterion, it can be reiterated that the HFs and HFs+DL ensembles were responsible for the best results in four of the five H&E-stained histological datasets investigated here. In these cases, the ReliefF + bGWO selection processes stand out with three occurrences. This indicates another pattern