## 8 Conclusions  
This research paper presented an innovative deep-learning-based model precisely designed to utilize the power of dual mammogram views: the craniocaudal (CC) and mediolateral oblique (MLO) views. This model's main objective is to diagnose comprehensively by simultaneously classifying mammograms based on their density, severity level/pathology, and abnormality type/findings. To achieve this, our model incorporates the state-of-the-art ConvNeXt as its backbone model. The design of this model is based on  
Figure 8: The ground truth (GT) labels and the labels predicted with the proposed model (PM) of each pair from two views. techniques like residual learning and transformer mechanisms, setting a solid foundation for advanced deep learning techniques. We utilized a channel attention mechanism based on squeeze-and-excitation to improve the ability of the model to concentrate on the most significant features and suppress the less important ones in the input feature maps. We employed an average-element-wise fusion method to consolidate the features' importance from both the CC and MLO views. This fusion method operates as a new layer within the model, seamlessly integrating the information extracted from the two views. This collaborative data merging ensures that no essential details are neglected, and the model acquires a holistic understanding of the mammogram image. Recognizing the diverse nature of the classification tasks, we introduced a multi-task/multi-branch architecture. This architecture tailors the feature-learning process to the unique requirements of each task: density, abnormality type, and lesion severity level. These tasks each have their distinct path within the architecture, facilitating more effective feature representation and task-specific learning. This enables our model to provide more accurate diagnoses for specific medical aspects. Employing multi-label learning helps enhance the model's ability to learn task-specific features and improves the model's performance. The proposed method was evaluated using benchmark datasets, the CBIS-DDSM and INBreast datasets, and outperformed SOTA. The proposed model is limited to masses and microcalcifications, and its extension to include other abnormalities will be the subject of future work. Conceptualization, E.A.-M. and M.H. ; data curation, E.A.-M.; formal analysis, E.A.-M. and S.A.A.-A. ; funding acquisition M.H. ; methodology, E.A.-M. and M.H. ; project administration, M.H. and H.A.A. ; resources, M.H. and H.A.A. ; software, E.A.-M.; supervision, M.H. and H.A.A. ; validation, E.A.-M.; visualization, E.A.-M.; writing--original draft, E.A.-M.; writing--review and editing, M.H. and S.A.A.-A. All authors have read and agreed to the published version of the manuscript. The authors extend their appreciation to the Deputyship for Research and Innovation of the Ministry of Education in Saudi Arabia for funding this research work under project no. IFKSUOR3-482-2. Not applicable. Public-domain datasets were used for the experiments. The CBIS-DDSM dataset is available at: [[https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM](https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM)]([https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM](https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM)) (accessed on 29 November 2023). The InBreast dataset is available at: [[https://biokeanos.com/source/INBreast](https://biokeanos.com/source/INBreast)]([https://biokeanos.com/source/INBreast](https://biokeanos.com/source/INBreast)), [[https://www.kaggle.com/datasets/martholi/inbreast](https://www.kaggle.com/datasets/martholi/inbreast)]([https://www.kaggle.com/datasets/martholi/inbreast](https://www.kaggle.com/datasets/martholi/inbreast)). (accessed on 29 November 2023). The authors declare no conflict of interest. The following is a list of symbols and terms used in this manuscript along with their definitions for reference.