## 3 Results and Discussion
### Feature Occurrences in the Main Solutions: An Overview  
To identify the descriptors present in the top solutions, as summarized in Table 6, a survey of the occurrence of each category of features in the first 10 solutions of each H&E-stained histological dataset was performed. To better understand the origin of each descriptor in each solution, the occurrences of the deep-learned descriptors are in Figure 5, CR, LG, and NHL datasets, and the handcrafted ones are in Figure 6, indicating solutions for LA, LG, OED, and NHL. It is important to highlight that the best solution for the LA dataset involved only HFs. Occurrences in NHL and LG also involved HFs due to the HFs\(+\)DL ensemble, justifying the representation of these datasets in Figure 6. Also, in these two datasets, the occurrence percentages were calculated relative to the total number of HFs in the HFs\(+\)DL ensemble, disregarding the percentages of deep-learned features.  
Considering the distributions illustrated in Figure 5, it is possible to verify some behaviors. The lowest occurrences occurred concerning the Inception v3 network descriptors, a maximum of 3.37% for the CR dataset, and there were no  
Figure 5: Occurrences of deep-learned features included in the best 10 solutions for the CR, LG, and NHL datasets.  
10 solutions for the LG set. On the other hand, the descriptors via DenseNet-121 and EfficientNet-B2 networks have the highest occurrences, especially for the NHL dataset, in which 63.45% of the features originated from the DenseNet-121 model. Descriptors via the EfficientNet-B2 architecture stood out in the solutions for the CR dataset with 38.46% of the occurrences surpassing more homogeneous occurrences (from 17.79% to 21.63%) for the deep-learned features from the ResNet-50, VGG-19, and DenseNet-121. Another homogeneous distribution can be seen in the LG dataset, involving the same descriptor origins as the CR set. In this case, occurrences ranged from 15.17% to 23.45%. When DL versus HFs totalization is considered, it can be seen that DL attributes predominated in the solutions for the LG and NHL datasets with occurrences of 65.17% and 97.16%, respectively. Despite these differences, it is not possible to indicate that these were the most important in the classifications.  
Concerning the occurrences of handcrafted descriptors (Figure 6), the lowest occurrence was of the box-merging fractal dimension (\(DF_{n}\) from [68]), since it was not selected for the top 10 solutions in three out of four histological datasets stained with H&E. This descriptor was present in the solutions for OED but with the lowest occurrence, only 1.84%. The probabilistic fractal dimension descriptor (\(DF_{p}\) from [24; 41]) was the second lowest occurrence but constituted the solutions for three of the four histological datasets. Another interesting result involves the enhanced version of Higuchi fractal dimension descriptor (\(DF_{H}\)[28]) with occurrences that surpassed those of the \(DF_{n}\) and \(DF_{p}\) approaches, which are widely explored in the literature [24; 41; 68], contributing to advancements in this particular research field. Finally, it is possible to observe the descriptors with the highest occurrences for each H&E dataset: lacunarity (45.80%) for LA; percolation (46.36%) for OED; LBPs (39.60%) for LG; and with a highlight, Haralick as the only ones that constituted the solutions for NHL.