## 2 Related Works  
A key limitation in WSI analysis is inconsistencies present within acquired data [25]. Without appropriate image correction, visual inspection of digital pathology is prone to significant errors. Similarly, this variability can compromise the accuracy of computerized classification models. Hematoxylin and Eosin (H&E) staining is a standard method used to highlight cellular and tissue structures in WSI. However, variations in H&E staining protocol across laboratories affect the consistency and reliability of the results [26]. The effect becomes particularly pronounced when a data-dependent AI-model is applied to diversely varied images. To address this issue, [PERSON] et al. [25] preprocessed the WSI using stain normalization by color transfer [27], prior to geometrical augmentation. An ensemble tree-based DL model was developed to classify different pathologies of breast tumors (BrT). The model weights were transferred from an AlexNet [28], with newly trained Fully Connected (FC) layers. While very high accuracy was achieved, its main limitation was reliance on several hierarchical models for indirect classification. Another subsequent study examined the advantage of image pre-processing on DL-based classification of breast cancer images [4]. In that study, histopathological images were processed using various filters, e.g., details, contour, and edge enhancements, along with histogram-based equalization. The classification process used VGG-16, VGG-19, MobileNet, ResNet-50, and DenseNet-121, with VGG-16 and VGG-19 exhibiting superior performance. However, an Analysis of Variance (ANOVA) revealed that the choice of filters was not statistically significant. In other words, with an optimal model architecture, determining features can be learned effectively, even without trivial enhancement. In fact, the process typically worked in reverse. CNN was utilized to enhance image quality, such as via fusion [29]. This suggests that salient features can be discovered and enhanced concurrently, as the learning progress. The Explainable AI (XAI) paradigm has attracted considerable attention from the CAD research community [30; 31]. In a recent study, an ensemble-based transfer learning approach was taken to rate dementia levels in Alzheimer's Disease (AD) [18]. The framework employed pretrained VGG-16, VGG-19, DenseNet-169, and DenseNet-201 for analyzing Magnetic Resonance Images (MRI). The classifications were ensembled to improve accuracy. The model was equipped with saliency and gradient weighted Class Activation Mapping (MAP) to highlight the neural regions influencing the decisions. The maps intuitively captured the pathological Regions of Interest (ROIs). The main conclusions of [PERSON] et al. 's work [4] raised an important question: What pre-processing strategies can then improve the learning mechanism, particularly for histopathological cancer image classification? Despite focusing on a different pathology, [PERSON] et al. [18] also presented results that led us to hypothesize that enhancing local texture appearance may effectively improve model performance, without relying on pixel-wise treatments [4]. This approach was explored across scientific disciplines. Contextual heuristic edge discovery and visual texture analysis were applied in barley grain grading using CNN [32]. Following grain ROI segmentation, a selected Gabor wavelet sub-band served as the input tensor. The accuracy surpassed baseline performance by over 10%, compared to ResNet-50, DenseNet-121, and VGGNet-19. A similar strategy has been used in contemporary research across multiple fields [33; 34; 35; 36]. However, their protocols appear to be restricted to isolated objects, making them unsuitable for WSI. Motivated by multi-focal image fusion proposed by [PERSON] et al. [29], and deep learning of explicit textual features proposed by [PERSON] et al. [32] and others, this paper examines the benefits of multi-band feature fusion in enhancing deep learning outcomes.