## 3 Results and Discussion
### Performance Overview against Different Approaches  
The best performances were observed respecting those obtained via traditional CNN architectures applied directly to the H&E-stained histological images. ResNet-50 [54], VGG-19 [53], Inception v3 [83], DenseNet-121 [84] and Efficient-Net-B2 [85] were the models  
Figure 6: Occurrences of handcrafted features included in the best 10 solutions for the LA, OED, NHL, and LG datasets. tested in this process, using the following: a fine-tuning process; cross-validation k-folds, with \(k=3\); 10 epochs; the stochastic gradient descent algorithm; initial learning rate of 0.01, decaying by 0.75 every two epochs; and, loss function as cross-entropy. Similar experiments were described by [29]. Also, the CNN models were applied using the transfer learning strategy [82], considering each network pre-trained on the ImageNet dataset [29; 32]. Thus, each dataset with a reduced number of examples was investigated using each model after a fine-tuned process, mapping the last corresponding layer of each architecture with the groups available in each H&E dataset. The final connections with their weights were updated based on the total number of classes in each context, ensuring appropriate results without overfitting. In addition, the input images were normalized according to the mean and standard deviation values of the ImageNet dataset [107]. The accuracy values the networks provide are shown in Table 7.  
To understand the differences between the distinction rates of the models proposed here and those obtained via networks applied directly, we consider the average accuracy values achieved in each H&E-stained histological dataset, as summarized in Table 6. Thus, it is possible to verify that the accuracy values via the proposed approach overcome those provided by the ResNet-50, VGG-19, Inception v3, DenseNet-121, and EfficientNet-B2 networks. The classification rates with the convolutional networks ranged from 74.27% to 98.89%. Therefore, the gains in accuracy ranged from 0.6% to 16%, approximately. The smallest gain occurred in the CR dataset (0.58%) and the largest (16.45%) occurred in the NHL set. For example, we increased the classification rate in the NHL dataset, which involves three classes, from 74.27% to 90.72%, illustrating an additional contribution of this study. Regarding the noted differences, the [PERSON] test was applied to verify if these solutions are statistically relevant. The Friedman test is a non-parametric statistical method capable of ranking the solutions under investigation, where the best option is set at the first position [108]. This type of test allows us to observe the variance of repeated measures and to analyze whether the existing differences are statistically significant via \(p\)-values. The smaller the \(p\)-value, the greater evidence that the difference is statistically relevant. It is possible to indicate that there is some relevant difference when the \(p\)-value is less than 0.05. In the experiments carried out here, the resulting \(p\)-value was 0.0004, indicating that the differences between the solutions are statistically significant. In addition, the Friedman test ranks the solutions as a table. The result involving the experiments is displayed in Table 8, with [PERSON]'s score indicated. The solutions obtained in this study are the most relevant for each dataset. It is important to note that when applying the Friedman test, each dataset represents a different sample (row) in relation to the corresponding solution. Each performance obtained through a solution in an H&E set has a rank value assigned based on the order of the best solutions. In the case of a tie, average ranks were assigned to the solutions. In each column, [PERSON]'s score was calculated as the average of the ranks of the samples, providing a final score for each solution [108].