## 6 Conclusions  
AI has played an increasingly active role in CAD of breast cancer. Thus far, existing research has primarily focused on developing sophisticated deep network architectures to address various challenges, achieving certain degrees of incremental accuracy. Despite promising successes, the developed CAD systems should still be supervised by pathologists to ensure valid slide interpretation. In contrast, this study investigated rather the impact of a prominent multi-band visual-enhanced filters, i.e., Gabor wavelets, on the DL-based classification of IDC using WSI modality. Our findings suggest that images fused by mean orientation invariant filters offer superior performance compared to max filters, as they preserve both cellular arrangement and tissue morphology well. By incorporating spatial relationships across broader extents through accumulated directional responses, the adopted fusion strategy closely aligned with pathological interpretation principles utilized by experts. Additionally, integrating Gabor filter responses into the input layer of ResNet-50 showed measurable contributions to improving its pattern association. Comparative analysis of its variants underscored the advantages of fusion on a larger scale, which achieved a strong balance between sensitivity and specificity. These results highlight the pivotal role of morphologically enhanced filters in enhancing diagnostic performance and optimizing DL models for BCa screening. This study assessed ResNet-50, VGG-16 and 19, and DenseNet-121, focusing on well-established architectures to provide a robust baseline for comparison. Our findings may provide a guideline for future research in ensemble DL strategies. While the models offer solid performance analyses, we acknowledge the omission of more recent and lightweight networks. Efficient modern architectures are worth considering for reducing computational complexity. Additionally, the reliance on a highly curated Kaggle dataset, with limited variability, poses a challenge in terms of generalizability in clinical settings. Specifically, domain shifts (e.g., variations in scanners, staining protocols, and patient populations) may affect model performance when applied to external datasets. Future research should focus on validation across more diverse, multi-institutional datasets, to improve real-world applicability and extend usability to other pathological conditions. Conceptualization, P.H. ; Data Curation, S.J. and P.H. ; Formal Analysis, S.J. and P.H. ; Funding Acquisition, P.H. ; Investigation, S.J. ; Methodology, S.J. and P.H. ; Project Administration, P.H. ; Resources, P.H. ; Software, S.J. ; Supervision, P.H. ; Validation, S.J. and P.H. ; Visualization, P.H. ; Writing--Original Draft, S.J. ; Writing--Review and Editing, S.J. and P.H. All authors have read and agreed to the published version of the manuscript. This work was supported by Suranaree University of Technology (SUT). Not Applicable. The data that supports the findings of this study are available in the breast histopathology images dataset [38] from Kaggle. They were derived from the resource available in: [[https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images](https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images)]([https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images](https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images)), accessed on 2 December 2024. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: