## 1 Introduction  
Histopathological analysis involves procedures that aim to investigate tissue samples that are commonly stained with specific dyes, such as hematoxylin and eosin (H&E) [1, 2],In these processes, specialists identify unusual alterations in the structures of cells and highlight potential abnormal health conditions, such as the diagnosis of cancer. It is noteworthy that this type of disease is a significant cause of early deaths worldwide and has high social and economic costs [3]. For instance, cancer is the second leading cause of death in the United States [4]. Thus, the early detection of diseases often enables less invasive treatments and increases the possibility of finding a cure and/or patient survival. The required steps in the preparation process of H&E images can influence the presentation of histological aspects, further increasing the difficulty of accurately diagnosing diseases under investigation. In addition to this problem, the analysis process takes time and may be susceptible to subjective interpretations by specialists [5; 6; 7]. These interpretation problems are mainly caused by human issues, such as subjectivity and fatigue. On the other hand, computer-aided diagnosis (CAD) methods play a fundamental role in this task since they can support specialists with second opinions [8; 9; 10], especially regarding H&E images [5; 8; 11; 12; 13; 14; 15; 16; 17]. In this regard, two categories of descriptors are typically investigated for the development of CAD systems. The first category consists of handcrafted features (HFs) defined by distinct extraction methods, usually aiming to overcome specific problems [18; 19; 20; 21; 22]. Among the HFs, it is possible to highlight techniques based on fractal geometry that use multiscale and multidimensional methods (Higuchi fractal dimension, probabilistic fractal dimension, box fusion fractal dimension, lacunarity and percolation) [23; 24; 25; 26; 27; 28; 29], Haralick [30] and local binary patterns (LBPs) [31]. For instance, Haralick and LBPs have been applied in several imaging contexts [32; 33; 34], exploring the identification of lung cancer subtypes [35], the presence of cancerous characteristics in breast tissue samples [18; 36] and the classification of colorectal cancer [6]. In addition, techniques that involve fractals at multiple scales and/or dimensions have also been applied to quantify the pathological architectures of tumors [23; 25; 26], demonstrating relevant results in the pattern recognition of prostate cancer [37], lymphomas [38], intraepithelial neoplasia [39], breast tumors [40], colorectal cancer [13] and psoriatic lesions [41]. Moreover, fractal methods are important for texture analysis because they provide information about the complexity of textures and patterns that are similar at various levels of magnification [42]. The second group of descriptors consists of deep-learned (DL) features obtained using convolution neural networks (CNNs) [43]. This group has been useful for defining different CAD approaches [15; 17; 22; 44; 45; 46; 47; 48] that consider data representations with multiple levels of abstraction [8; 47; 49]. The most explored models have provided the best accuracy rates on the ImageNet dataset [50], such as AlexNet [51], GoogleNet [52], VGGNet [53], ResNet [54], Inception [52] and other applications [20; 32; 55]. Despite the advances provided individually by DL features and HFs, investigations have been carried out to develop models based on combinations of these features [18; 21; 55; 56; 57; 58; 58], generating strategies known as ensemble learning (EL) [20; 59]. Moreover, these studies have indicated no single solution for distinct datasets. EL models can also consider distinct classification algorithms in order to obtain more accurate single decisions by applying ensembles of classifiers [20; 60]. This type of association has provided important results in the study of cervical cell imaging [20].