## 2 Related Studies
The best achieved accuracy value was 55% for round vs. oval classification, and 84% in case of irregular vs. stellar classification. [PERSON] et al. [47] adopted the genetic algorithm to select the most significant hand-crafted features out of the total 130. Finally, the back-propagation neural network was employed for the classification task on round, oval, lobular, and irregular shapes that reached 84.5% accuracy on the digital database for screening mammography dataset. Several studies in the literature dealt with the breast cancer imaging and XAI. [PERSON] et al. proposed a binary classification framework based on AlexNet and VGG-19 architectures to recognize the presence or absence of mass lesion on DBT image of two in-house datasets [37]. The authors adopted the Grad-CAM method to study the behavior of the classifiers, i.e., whether they align with the delineated lesion labeled by the expert radiologists. Employing the Grad-CAM method, the authors concluded that central areas of the lesion contribute more towards classification, whereas the branches of the tumor bring less impact on classification. [PERSON] et al. performed multiclass classification of ultrasound breast images considering benign, malignant, and normal classes on two public datasets [48] using eight pretrained CNNs and a custom model. The highest accuracy among the pretrained architectures was achieved by ResNet-50 with a value of 92%, whereas the customized model achieved 100% accuracy. For explaining the classification mechanism and to study the performance of the customized model, the Grad-CAM heat map visualization was also incorporated. [PERSON] et al. compared the binary classification performance of DenseNet-169 and EfficientNet-B5 models on predicting the availability of malignancy of the lesions from the mammogram images on an in-house dataset [36]. The former network achieved an accuracy of 88.1%, whereas the latter reached to 87.9%. The Grad-CAM method was used merely to spotlight the important regions over an image that lead to the classification. The authors claimed that Grad-CAM also spotlights the surrounding areas of the tumor, which shows the importance of not only the tumorous region but also the nearby regions. Similarly, [PERSON] et al. [49] proposed a framework driven by a custom model and a pretrained (ResNet-50) architecture to classify the benign and malignant masses on two publicly available mammogram datasets. The authors reached an accuracy of 83.75%. The Grad-CAM is employed to examine the spatial position of the object located by the CNNs. The authors claim that in case of successful classification, the XAI method highlights the mass correctly, however, it may also focus on the irrelevant regions due to spots that are not lesions. Apart from the unavailability of explainability in majority of the existing articles dealing with DBT image classification task, only few authors [28; 29; 30; 47] considered the shape-based cancer classification of the lesion, which not only distinguishes among the normal and abnormal images but also highlights the growth pattern of the tumor shapes. Unlike the proposed multi-class morphological CAD classification framework in this study, most of the authors merely focused on malignant vs. benign classification of the lesion [26; 41; 42; 43; 44; 46] and provided no, or unsatisfactory, XAI discussion in some cases. The only two authors which provided XAI in their CAD system [36; 37], limited it to the Grad-CAM method, and did not consider more complex classifications of the breast cancer, such as the shape one investigated by this study.