###### Abstract  
Convolutional neural networks and deep learning models represent the gold standard in medical image classification. Their innovative architectures have led to notable breakthroughs in image classification and feature extraction performance. However, these advancements often remain underutilized in the medical imaging field due to the scarcity of sufficient labeled data which are needed to leverage these new features fully. While many methodologies exhibit stellar performance on benchmark data sets like DDSM or Minimias, their efficacy drastically decreases when applied to real-world data sets. This study aims to develop a tool to streamline mammogram classification that maintains high reliability across different data sources. We use images from the DDSM data set and a proprietary data set, YERAL, which comprises 943 mammograms from Mexican patients. We evaluate the performance of ensemble learning algorithms combined with prevalent deep learning models such as Alexnet, VGG-16, and Inception. The computational results demonstrate the effectiveness of the proposed methodology, with models achieving 82% accuracy without overtaking our hardware capabilities, and they also highlight the efficiency of ensemble algorithms in enhancing accuracy across all test cases. convolutional neural networks; ensemble learning; deep learning; transfer learning; image classification; medical imaging; mammography +
Footnote †: journal: Medical Image Classification  
1 Facultad de Ingenieria Mecanica y Electrica, Universidad Autonoma de Nuevo Leon, Av. medical imaging requires rigorous expert review to label each image type appropriately. As a result, there is a scarcity of free-to-use data sets, creating a bottleneck for the further exploration of deep learning applications. Other concerns include the limited representational scope of these free-to-use data sets in the practical application of an ML model. Studies have indicated that algorithms that perform well on benchmark data sets often falter in real-world clinical scenarios [11]. A significant contributor to this discrepancy is data set bias, which frequently occurs when the training set's population distribution differs from the target set's, commonly known as the test set [12]. Image annotation, despite being time-consuming and requiring a large team of specialists, poses an even greater challenge when constructing models with limited labeled images. Models need more data to converge to a good performance. This demand for resources is particularly detrimental in low-income and middle-income countries, where the need for diagnosing medical images outstrips the available specialist capacity [8]. In this work, we examine a mammography classification case from a data set called YERAL, obtained from a specialized Mexican Oncology center. YERAL represents our target data set for optimizing the diagnosis process using ML models. The driving factor behind focusing on this particular medical issue is the staggering volume of data accumulating each year, outpacing the number of medical specialists available to handle it, despite the Mexican government's best efforts to create awareness. We introduce a novel approach by leveraging ensemble learning in scenarios where the state of the art, like deep learning and cutting-edge methods, does not fit because of data and computational resource constraints. While others might hit a roadblock with limited resources, we have merged elements of deep learning with traditional machine learning tools. This hybrid approach allows us to break down the data, making them more digestible. Our innovative use of ensemble learning in this context not only streamlines training complexity but also serves as a pioneering contribution to the field, demonstrating new possibilities for those facing similar limitations. The article's organization is as follows: We first discuss the problem we found about medical imaging results with existing deep learning and ML models that, when applied to our data set, received lower performance levels.