## 4 Conclusions  
In this work, an ensemble learning method was elaborated through multiple descriptors (handcrafted and deep-learned features), a two-stage feature selection, and a classification process with five algorithms (heterogeneous ensemble). The approach was utilized to categorize H&E histological images that are representative of various datasets, such as colorectal cancer, liver tissue, oral dysplasia, and non-Hodgkin's lymphomas. The best ensembles indicated average accuracy values ranging from 90.72% (NHL) to 100% (CR). Since the initial feature set was composed of 11,086 values (462 handcrafted descriptors and 10,624 deep-learned features), the best solutions used a maximum of 53 features, with the following scenarios being noteworthy: CR with only 21 descriptors via bGWO; OED with only 11 descriptors via PSO; LA with 40 descriptors via bGWO; LG with only 29 attributes, through the bGWO; NHL with 53 descriptors, via GA. A breakdown of the main descriptors was also presented. It was observed that deep-learned descriptors predominated in relation to handcrafted ones, especially in the solutions for the LG and NHL datasets, with occurrences of 65.17% and 97.16%, respectively. On the other hand, the best solution for the LA dataset involved only handcrafted attributes. Another interesting behavior regarding handcrafted attributes is that the improved version of the Higuchi method outperformed the occurrences of important fractal techniques, specifically \(DF_{n}\) and \(DF_{p}\), indicating the potential of the descriptor in multiple H&E-stained histological \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Method** & **Accuracy (LA)** & **Accuracy (LG)** \\ \hline Proposed Method & LA: HFs \(+\) (ReliefF \(+\) bGWO); LG: HFs\(+\)DL \(+\) (ReliefF \(+\) bGWO) & 99.24\% & 100\% \\
[29] & ResNet50 with fine-tuning, multidimensional and multiscale fractal features & 99.62\% & 99.62\% \\
[15] & ResNet50 (activation, 48\_relu layer), ReliefF and 5 deep-learned features & – & 99.32\% \\
[57] & Inception-V3, Fractal Dimension and Lacunarity (DL\(+\)HFs) & – & 99.25\% \\
[109] & CNN for texture & 99.10\% & 98.20\% \\
[112] & GIST handcrafted descriptor & 88.40\% & 93.70\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Accuracy rates indicated in different approaches for distinguishing LA and LG images. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Method** & **Accuracy** \\ \hline Proposed Method & HFs \(+\) (ReliefF \(+\) bSO) & 100\% \\
[17] & OralNet: Fused Optimal Deep Features & 99.50\% \\
[110] & Neural architecture search and handcrafted descriptors (morphological and non-morphological) & 95.20\% \\
[111] & Handcrafted descriptors (SIFT, SURF, ORB) & 92.80\% \\
[100] & Handcrafted descriptors (morphological and non-morphological) & 92.40\% \\
[16] & Densenet121 & 91.91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy rates achieved in different methods for OED image classification. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Method** & **Accuracy** \\ \hline Proposed Method & DL \(+\) (ReliefF \(+\) bGWO) & 100\% \\
[29] & ResNet50 with fine-tuning, multidimensional and multiscale fractal features & 99.39\% \\
[15] & ResNet50 (activation, 48\_relu layer), ReliefF and 35 deep-learned features & 98.00\% \\
[56] & 8 CNN models, handcrafted descriptors & 97.60\% \\
[20] & 9 CNN models, handcrafted descriptors & 97.50\% \\
[62] & Le-Net, multidimensional and multiscale fractal features, Haralick and LBPs & 91.06\% \\
[14] & ResNet deep-tuning (DL) & 86.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy values provided by different strategies for CR image classification. datasets. In addition, the handcrafted features with the highest occurrences were lacunarity (45.80%, LA dataset), percolation (46.36%, OED dataset), LBPs (39.60%, LG dataset) and Haralick (100%, NHL dataset).