## 1 Introduction
Another highlight is the model presented by [59], who conducted a comparative study between a logistic regression classifier trained only with DL features, an ensemble of HFs and an ensemble of all features. The authors concluded that the ensemble involving all features delivered the best distinction rates. In the context of using fractal techniques with CNN models, the method presented by [29] considered an ensemble involving two CNN architectures: one pre-trained with histological images and the other pre-trained with artificial images, which were generated using features from fractal techniques. The authors concluded that their proposal outperformed classification algorithms and CNN models when applied separately. It is essential to observe that EL models can be developed with the most relevant features, exploring a single selection stage to reduce the search space and increase the accuracy of the system [13; 15; 18]. The use of feature selection through two stages can also be implemented [61; 62; 63]. In these cases, the strategies were applied to the ensemble of DL, and the results obtained were better than those obtained via a single stage. Thus, this approach can provide a reduced number of main compositions for developing CAD systems, making knowledge even more comprehensive for specialists. Moreover, the use of this combination is not restricted to image analysis with promising solutions in the frequency domain [64]. The versatility of EL strategies was observed with different combinations between features and classifiers to investigate some types of histological images [20; 21; 29; 32; 55; 56; 59; 65; 66], but not as described here, which were addressed to define patterns of techniques through multiple H&E datasets. Some examples of EL strategies that can still be investigated are the HFs, DL features, and the HFs and DL features, all of which are in a classifier ensemble context. The best configuration can be compared via classifications only with the use of CNN models, which are useful to indicate the pertinence of using ensemble learning in pattern recognition of various H&E images (colorectal cancer, oral dysplasia, non-Hodgkin's lymphoma, and liver tissue). Moreover, the previously indicated EL models can also be explored via feature selection in two stages, which is a valuable approach to present more optimized solutions, in addition to significantly reducing a high-dimensional search space, such as those explored here. Thus, known problems such as overfitting or underfitting are minimized [67]. An EL model capable of providing the main solutions for various H&E images, with robust computational approaches for pattern recognition, can significantly improve CAD systems and make knowledge more comprehensive for specialists. This work presents an EL approach to classify histological images from different contexts. The proposal explored multiple handcrafted features through multidimensional and multiscale fractal techniques (Higuchi fractal dimension, probabilistic fractal dimension, box fusion fractal dimension, lacunarity, and percolation), Haralick and LBPs descriptors, and deep-learned descriptors, which were obtained from several convolutional neural network architectures. Moreover, a two-stage feature selection (ranking with metaheuristic algorithms) with a heterogeneous ensemble of classifiers completed the proposed method to indicate the best solutions. The first stage of selection was defined through the Reli-eff algorithm. In the second phase, the approach to discover the most effective features within each reduced subset was employed, exploring particle swarm optimization, genetic algorithm, and binary gray wolf optimization. Each result was verified through a robust ensemble process with a Support Vector Machine, Naive Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbors. This proposal provided the following contributions:  
1.