## 2 Related Studies  
Over the last decade, because of the superior aptitude to capture cancers, the DBT has become the new gold standard for the digital mastography [40]. Alongside this, machine learning has revolutionized the medical field by offering automatic detection, segmentation, and classification of the cancer [24; 25; 41; 42; 43; 44]. Moreover, XAI has additionally elevated the confidence of the research community to apply DL in the computer-assisted treatments [34] by uncovering the black box and hidden nature of the DL. This helps building confidence on the usage of AI in clinical settings, also paving the way towards the DL-centered image-guided CAD systems. This section presents and discusses the most relevant and original studies dealing with automatic breast cancer classification and the incorporation of the XAI techniques on the breast cancer imaging recently. The shape of the breast tumors leads to diagnosis of the different types and stages of the cancer [26]. Generally, the breast cancer is morphologically categorized into five shapes based on tumor growth pattern, named round, oval, lobulated, irregular, and stellar [27], as depicted in Figure 1. Numerous authors claim that the transition from the round shape to stellar shape of the cancer is the journey from benign to malignant cancer [26; 29; 45]. The shape-based breast cancer classification of mammogram images at RoI level using generative adversarial network (GAN) and CNN is presented by [PERSON] et al. [29]. The authors used a publicly available dataset for validation and achieved an overall classification accuracy of 80% for irregular, lobular, oval, and round shape classes. Similarly, [PERSON] et al. [28] proposed a multi-task loss CNN architecture based on the Faster R-CNN model to detect tumor lesions by considering irregular, round, and oval shapes of the breast cancerous lesions using in-house and publicly available datasets. Their approach generated bounding boxes around the tumor, and then used the semantic descriptors to identify the lesion shape inside the RoI. The accuracy on in-house and public datasets reached 88% and 82%, respectively, where both accuracy values were computed on accurately labeled data for testing purposes. In a previous study by authors [39], two different approaches for the classification of DBT images into four lesions, i.e., irregular opacity, regular opacity, stellar opacity lesions, and no lesions, were implemented and tested on an in-house dataset. The first approach utilizes an artificial neural network that takes morphological and hand-crafted features extracted from the RoI images and performs classification. The second framework encompasses the pretrained CNNs without requiring the hand-crafted features. The authors claimed that the VGG network outperformed the other pretrained architectures by reaching 91.61% and 81.49% accuracy with and without augmentation. A GAN-based interpretable CAD system for the classification of oval, round, irregular, and lobular shapes on the mammogram images was devised by [PERSON] et al. [30]. The CADsystem was tested on a public dataset that managed to achieve 71% accuracy on the lesion shape classification. A study on mammogram and MR scans on three publicly available datasets was conducted by [PERSON] et al. [26] to classify the shapes of the tumorous regions using geometrical feature-based classifier. Since the authors merely considered the binary classification problem (benign lesion vs. malignant lesion), unlike previously explained methods, the reported accuracy, i.e., 91.4%, was pretty high. A recent study by [PERSON] et al. used SVM, random forests, naive Bayes, and multilayer perceptron methods to classify the breast lesions on tomosynthesis images [46]. The authors also considered radiomic features along with the shape of the lesion. All the round and oval tumors were labeled benign, whereas the irregular and the stellar were labeled malignant on an in-house dataset.