## 2 Background  
In recent years, AI applications have rapidly developed and improved due to substantial public interest. Market forces stand to gain from applying deep learning in natural language translation, photo captioning, speech recognition, and self-driving cars [8]. The continuing success in applying these models has often caused the desire to project these same results into other areas. Medical imaging stands out in this context. The correct and prompt interpretation of diagnostic information is pivotal and, as such, remains heavily reliant on human expertise [13]. Such a paradigm often presents challenges, especially considering the voluminous medical images requiring diagnoses and the limited number of specialists available for interpretation. While numerous studies have demonstrated that deep learning models can achieve expert-level performance in medical imaging, there is still room for improvement in reporting standards. Comprehensive surveys, such as those by [8; 14], have highlighted a common concern: methodologies and studies often lack completeness and do not conform to a standardized approach for presenting results. Such inconsistencies in reporting often compromise the reliability of interpretations and challenge the replication of results with different data sets. A notable observation from these surveys is that a large proportion of publications emphasize the application of convolutional neural networks (CNNs). Within these works, reference is frequently made to renowned architectures like Alexnet [15], VGG-16 [16], ResNet [17], and Inception [18]. CNNs have gained substantial acclaim in the medical domain due to their innate capability to automatically learn features that help to distinguish between classes of various computer vision tasks, and there are many examples where they achieve noteworthy performances [19; 20]. As the field has evolved, an array of architectures has emerged, each presenting its own set of advantages and limitations. This diversity offers users the flexibility to select the parameters best suited to their needs. The evolution of CNN architectures has been marked by significant milestones, one of which is the GoogLeNet or Inception network [18]. This architecture heralded a new era in computer vision, thanks to its distinct inception block layer. Due to its features, it has become a preferred choice for transfer learning in breast imaging, encompassing a broad spectrum of images beyond just mammography [21]. Subsequently, the ResNet architecture [17] made another big step for complex classification problems by introducing the concept of residual learning. With an impressive structure of over 100 layers and more than 11 million parameters, ResNet underscores the belief that deeper CNNs generally produce better outcomes. This assertion was further accentuated by identifying bottlenecks in the VGG-16 network, where increased data complexity led to a diminished generalization capability. In a similar path to CNN architectures, the practice of transfer learning (TF) has also garnered considerable attention [7; 22; 23]. One of the main issues attached to medical applications for deep learning is that there is usually a limited number of annotated data, where the problem of overfitting arises [24]. This challenge comes from a limited number of available annotated training samples. Transfer learning helps with this problem by using pretrained models from nonmedical images, fine-tuning the network parameters to fit our data later, or using it to perform feature extraction [25]. A literature review about transfer learning for medical imaging classification [26] describes the backbone models for transfer learning, leaving Alexnet and VGG-16 as shallow and linear model types and Inception and ResNet as deep model types. The authors detail the importance of identifying the eligibility of these networks for different types of data. Research has demonstrated that feature extraction by CNNs can notably boost classification tasks [27].