## 3 Results and Discussion
### Performance Overview against Different Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CNN Architecture** & **CR** & **OED** & **LA** & **LG** & **NHL** \\ \hline ResNet-50 & 96.73\% & **96.00\%** & **91.48\%** & 97.78\% & **74.27\%** \\ VGG-19 & **98.67**\% & 92.67\% & 77.96\% & 91.98\% & 65.07\% \\ Inception v3 & 85.64\% & 90.50\% & 85.37\% & 92.59\% & 67.07\% \\ DenseNet-121 & 97.45\% & 93.17\% & 87.41\% & 93.98\% & 72.40\% \\ EfficientNet-B2 & 96.12\% & 94.67\% & 90.00\% & **98.89\%** & 70.27\% \\ \hline \hline \end{tabular} The best accuracy rates for each dataset are highlighted in bold.  
\end{table} 
Table 7: Accuracy provided by CNN architectures after fine-tuning process: best results are in bold. Finally, we believe that the heterogeneous ensemble of classifiers was another relevant factor in achieving the results listed previously. We were able to define a combination of algorithms that supported the pattern recognition process of different types of H&E images with a more robust and reliable system capable of covering the weaknesses that may exist in a single classifier. In addition, we believe that the bias and variance have been reduced, minimizing the overfitting. More comparisons or algorithms could be considered to indicate the possible limits of each solution or even whether the main combinations are maintained from more descriptors or selection methods. However, the set of techniques with their associations and experiments described here provided an important overview of the potential and discriminative capacity regarding H&E-stained histology images. Observations Based on Related Work  
Considering the best results (Table 6), an overview is presented in relation to the literature involving each H&E-stained histological dataset. It is essential to note that even though the images share the same type, the datasets are not similar in the number of examples, classes, metrics used, validations, or distinct samples. Therefore, the purpose of this validation is not to compare performance rates directly. This situation requires equal conditions between the models: for example, by reproducing each strategy in the H&E-stained histological datasets explored here. This task is difficult or even infeasible. Hence, it is expected to observe whether the obtained solutions provide results among those available in the specialized literature. This illustrative overview is displayed in Tables 9-12 for the CR, OED, LA and LG, and NHL datasets, respectively. From the values collected for this type of observation, it is verified that the solutions obtained here could provide highly competitive accuracy values, especially in three (CR, OED, and LG) of the five H&E datasets. In relation to the LA dataset (Table 11), the proposed approach provided an accuracy very close to the best solutions available in the literature: 99.24% (our solution) against 99.62% [29], which is a difference of only 0.38%. Taking into account the NHL dataset, even though the obtained solution indicated an accuracy among those achieved in the proposals of [62; 109], a better-defined difference is verified in relation to the results of [20; 29; 56], showing that the strategies explored here deserve more attention for this type of image. Nevertheless, this overview has shown the discriminative capacity of the solutions obtained for different types of histological images, considering strategies not yet explored in the literature. Moreover, the ensemble learning approach provided relevant solutions with important information about the best combinations of descriptors and selection methods, using a reduced set of descriptors and revealing their main occurrences to recognize possible patterns in four types of histology tissues.